© Higher Education Press and Springer-Verlag Berlin Heidelberg 2012 Revised
December 04, 2022

# $\bm{The~{}Prominence~{}of~{}Artificial~{}Intelligence~{}in~{}COVID-19}$

MD Abdullah Al Nasim 1 Aditi Dhali 1 Faria Afrin 1 Noshin Tasnim Zaman 1
Nazmul karim 2 Anika Tabassum Sejuty 1 Kazi A Kalpoma 3 Md Mahim Anjum Haque 1
1 Department of Research and Development, Pioneer Alpha
2 Electrical and Computer Engineering, University of Central Florida
3 Department of Computer Science and Engineering, Ahsanullah University of
Science and Technology

###### Abstract

In December 2019, a novel virus called COVID-19 had caused an enormous number
of causalities to date. While the front-line doctors and medical researchers
have made significant progress in controlling the spread of the highly
contiguous virus, technology has also proved its significance in the battle.
Moreover, This survey paper explores the proposed methodologies that can aid
doctors and researchers in adopting early and inexpensive methods to diagnose
the disease. Most developing countries have difficulties carrying out tests
using the conventional manner, but a significant way can be adopted with
Machine and Deep Learning. On the other hand, the access to different types of
medical images has motivated the researchers. As a result, a mammoth number of
techniques are proposed. This paper first details the background knowledge of
the conventional methods in the Artificial Intelligence domain. Following
that, we gather the commonly used datasets and their use cases to date. In
addition, we also show the percentage of researchers adopting Machine Learning
over Deep Learning. Lastly, in the research challenges, we elaborate on the
problems faced in COVID-19 research, and after analyzing it, future directives
are suggested to build an advanced, more developed health industry.

###### doi:

††articletype: REVIEW ARTICLE

COVID-19, Medical Image, Deep Learning, Machine Learning, Life Sciences,
Epidemic, Coronavirus

##  1 Introduction

After the flu of 1918, the novel Coronavirus or COVID-19 became the fifth
documented outbreak and was discovered first in Wuhan; China [60]. Gradually,
it spread in the whole world. The International Committee on Taxonomy of
Viruses gave the official name of the Coronavirus as "Severe Acute Respiratory
Syndrome Coronavirus 2" or SARS-CoV-2 [61]. The World Health Organization
(WHO) announced the SARS-CoV-2 outbreak as a Public Health Emergency of
International Concern on January 30, 2020, and a pandemic on March 11, 2020 It
got reports of a cumulative total of 179,036,457 confirmed cases globally
until October 13, 2022, according to PAHO.

Figure 1: The Transmission Mechanism of COVID-19 from One Species to Another

If we look into the history, [138], considered bats as the natural host of
SARS-CoV-2. Moreover, [79] believed that bats are a locus for its expansion.
According to [78], Coronavirus infection which arrived from China increased
anxiety about human-to-human transmission. [88] explained that human-to-human
communication of 2019-nCoV is similar to the SARS-CoV of 2002. The authors
suggested giving extra attention to the prevention of COVID-19. They showed
that it is possible to end the epidemic according to the SARS-CoV of 2002
experience. In that case, the screening and control system at transportation
stations and airports may play a vital role in preventing the outbreak of
COVID-19. Fig. 1 shows the transmission of the Coronavirus.

From the ending of 2019, countries started to take precautions to prevent its
outbreak. Though they started to maintain strict lockdown, curfew, inspire
wearing face mask, regulate social distancing, the rate of positive COVID-19
cases and deaths increased significantly.

Figure 2: The COVID-19 case and death data of the Pie Chart was Collected from
World Health Organization on December 4, 2022

Fig. 2 shows the percentage of Daily COVID-19 cases deaths on December 4,
2022, based on the top ten most affected countries so far. It is cleared that
the number of total death case is more prominent in the United States than in
the other nine countries now. The second most death cases are found in Brazil.
After Brazil, there are the most founded death cases, respectively in South
Korea and Japan but there death rate is comparatively pretty low than the
other countries. The lowest percentage of death were in South Korea among
these top ten countries which is 0.46%.

Staring from the very beginning,within March 18, 2020, the number of COVID-19
patients and deaths were over 30,000 and 2500 [106]. At this point, the total
number of affected cases, death cases, and recovered cases around the world
from the very beginning of this pandemic till November 28, 2022, is depicted
in Fig. 3. Affected cases, death cases, and recovered cases based on different
countries are shown in Fig. 4. We gathered these data from online
(worldometer.info, New York Times, and pharmaceutical-technology.com). The
United States of America tops this list with a total number of affected cases
97,618,392 when total death cases are 1,071,245 and the recovery rate is
approximately 98.9%.

Figure 3: Total Affected Cases, Death Cases and Recovered Cases Around the
World until 28 November, 2022 Figure 4: Total Affected Cases, Death Cases and
Recovered Cases of Different Countries until 28 November, 2022 Figure 5: The
Percentage of People Vaccinated with Respect to t heir Countries until 11
July, 2021

Following the discovery of the COVID-19 pandemic, the importance of the models
in overcoming COVID-19 challenges has multiplied. Taking these factors into
consideration, in this paper, we have conceptualized and comprehensively
represented a systematic review of the recent research on Machine Learning
(ML) and Deep Learning (DL) in detecting COVID-19. This paper aims to
effectively identify the ML and DL algorithms that helped in the early
detection of COVID-19.

Figure 6: Flowchart of Our Survey Work. It Demonstrates the Workflow from the
Identification of Research Aim till the Collection of the Prominent Researches
over the Recent Years in the COVID-19 Research Community

The whole work we have done in this paper is depicted in this Fig. 6. At
first, we looked for research papers into famous online databases such as
Elsevier, ERS journals, IEEE Xplore, Springer, Google scholar, Hindawi, MPDI,
science direct, etc. On different websites, we have searched with some common
keywords regarding the detection of COVID-19 using ML and DL methods and
gather more than 700 papers. A graphical representation of different
publishers and the number of documents we have used from those to develop this
work has been depicted in Fig. 7. We have filtered out more than 300 papers
focusing on the documents that have been used for early detection or solving
COVID-19 related issues. Exclusion procedures for filtering more than 250
publications that apply to COVID-19. Finally, we have reviewed a total of 100
papers related to ML and DL models in the detection of COVID-19 and some non-
ML and DL algorithms that use image processing and computer vision algorithms
in the early detection of COVID-19 and other COVID-19 related crises. In
total, 100 papers, 35 papers used ML frameworks, 60 articles used DL
algorithms, and five papers on non-ML and non-DL algorithms. We have separated
them into different parts: methodology, summary, and the complete overview of
other research work using tables. Data derived from each research paper
evaluated for the analysis also include dataset used, the tool used, type of
input used, augmentations technique, number of images used, accuracy, and
other measures are highlighted.

Figure 7: Plot to Demonstrate Our Collection of Research Papers from Different
Renowned Publishers Figure 8: The Common Methodology of Deep Learning in
Training and Evaluating Different Models with COVID-19 Datasets

##  2 Background Study

Deep learning is playing a big part in COVID 19 detection. A significant
number of strategies for identifying COVID 19 patients have already been
developed by researchers. In Fig. 8, we try to convey the core of the
situation. We can state that we examine and follow this flowchart in most of
the paper to draw their Coronavirus prediction utilizing DL. Most of them use
CT (Computed Tomography) or real-time reverse transcription-polymerase chain
reaction (real-time RT-PCR) or CXR pictures of the lungs as input (Chest
X-ray). Though CT scans are not always as accurate as RT-PCR, they are less
expensive, more accessible, more scalable, making them an intriguing choice
for researchers. However, RT-PCR has a high specificity, its sensitivity
changes with time. The data is then preprocessed using various data
augmentation, normalization, and other procedures to improve the structure and
produce a vast dataset. Many of the authors cited a tiny dataset as one of
their constraints. These methods can assist one in dealing with specific
situations. Following that, several features are retrieved to use the given
strategy ideally. Data is separated into training and testing sets during the
processing step. To carry out the tests, the authors utilized various ratios
of training and testing datasets. They do the fine-tuning and cross-validation
if the result is not satisfactory enough. Here, as the model, some used the
existing DL classifiers, such as ResNet, EfficientNet-B0, MobileNetV2,
DenseNet, Inception V3, or SqueezeNet, etc. Some other authors have applied
their proposed model here. For example, Covid-resnet [31], Covid-caps [3],
MetaCOVID [102], Ensemble-CVDNet [70], COVIDScreen [104], DL-CRC [93], Covid-
fact [44] etc. As output, they reveal the prediction. Some predicted whether
the input belongs to a COVID-19 patient or not. Some identified COVID 19
patients from other pneumonia patients or virus-affected patients. Evaluation
or validation is done by accuracy, AUROC, F1 score, AUC, or recall.

AI approaches have the potential to provide significant benefits in combating
COVID-19, but it is essential to consider the advantages against the potential
risks and limitations. AI algorithms can analyze vast amounts of data quickly
and accurately, helping to identify patterns and trends that may be missed by
humans. AI can predict the spread of COVID-19 and help authorities to plan and
allocate resources efficiently.AI can help in drug discovery by analyzing vast
amounts of data to identify potential treatments and vaccine candidates. AI
requires vast amounts of data to function effectively, and there may be
limited data available in the early stages of the pandemic, hindering AI’s
effectiveness. AI-based contact tracing and surveillance measures may raise
privacy concerns among the public. AI-based contact tracing and surveillance
measures may raise privacy concerns among the public. Careful consideration
and collaboration between AI developers, healthcare professionals, and
policymakers are essential to ensure that AI technologies are deployed
effectively and ethically.

##  3 Datasets

Most scientific research depends on the collection and experiment of
measurement data. Some datasets are private, and some are public for the
authorities, politicians, researchers, and doctors. These scientific data sets
are applied to develop new algorithms and differentiate those from other
algorithms in the particular study. Nowadays, some datasets such as medical
image data, electronic health records, country-wise data, public data, etc.,
are being used constantly for scientific research.

###  3.1 Medical Image Data

Medical image data are gathered from X-rays, CT scans, MRI, intra-operative
navigation, and biomedical research. It is the primary data source in medical
science. Some renowned medical image data sources for COVID-19: Cohen Dataset,
Praveen Corona Hack: Chest X-Ray Dataset, Mooney P. Chest X-ray Images
(Pneumonia) Kaggle Repository, Pneumonia Dataset, CORD-19 dataset [119].

###  3.2 Electronic Health Record

Electronic health records, or EHRs, are real-time data collecting systems that
save patient information in a digital format and make it accessible to
authorized users quickly and securely. The patient’s medical history,
diagnosis, radiological images, and test results are all included. Medical
errors are reduced, and patient data is protected, thanks to electronic health
records. The most commonly utilized EHR datasets include Research Electronic
Data Capture (REDCap), JSRT Dataset (Japanese Society of Radiological
Technology), Covid-19 Radiography Database, and Italian Society of Medical and
Interventional Radiology (SIRM) COVID-19 Database.

###  3.3 Country Data

The country dataset is the resource where users get different variables for
specific countries. Country data provides transparency and creates value to
the community by making it publicly available. Some authors used country data
in the papers: [141] developed their model based on this. The Israeli Ministry
of Health publicly disclosed dataset The dataset contains primary records of
all the residents who were tested for COVID-19 countrywide. [28] collected the
Covid-19 recovered cases, confirmed cases, and deaths from the Saudi Ministry
of Health website. They used the dataset as time-series data to train their
proposed model.

USA based Covid-19 outbreak analysis was done by [41] and [13]. [10], [24],
[21], [125], [103] analysed Covid-19 related data of Brazil. [14], [128],
[114], [38], [7] worked on Indian Covid-19 cases. Researchers studied bout
country-wise characteristics, spreading rate, the mortality rate of Covid-19
in their research.

Some other datasets are COVIDiSTRESS [129] that analyze behavioral
consequences of Covid-19, Geocov19 note qazi2020geocov19 that consists of a
vast number of multilingual Covid-19 related tweets. Melo et al. also came up
with the first public dataset from Brazilian twitter [23]. This dataset
extends the Covid-19 research from a different perspective. Some popular
datasets used in various research works related to COVID-19 are referenced in
Table. LABEL:Dataset_table_1.

##  4 Deep Learning in COVID-19 Battle

Machine learning has begun to shine a light on hope in the face of the
COVID-19 pandemic in this unclear and dire circumstance. ImageNet
Classification, a CNN-based DL solution, earned significant success in the
best-known international computer vision competition [109], which sparked the
adoption of ML in medical imaging area. Experts utilise ML to anticipate the
unknown future by predicting who is most likely to be affected, the danger of
infection, diagnosing patients, developing treatments faster, and many other
things.

###  4.1 Artificial Neural Networks In COVID-19

[120] created a COVID-19 diagnostic DL model that was utilized on chest
radiographs and categorized the X-ray images using transfer learning and
combination-based COVID-Net. They developed ResNet-101 and ResNet-152 and
trained on the ImageNet dataset according to the precision and loss rate after
correcting and integrating the transfer learning model with a modified deep
network layer. [87] has categorized the status of COVID-19 patients’ health
into infected, uninfected, exposed, and susceptible. They have used Bayesian
and back propagation algorithms.

[67] used a Deep Neural Network to identify COVID-19 from x-ray pictures and
CT scans using binary and multi-class classification in a study. Their
proposed model is a 24-layer 6 CNN Network for X-ray and RT-PCR
categorization. [66] used a deep transfer learning-based strategy to predict
COVID-19 patients using chest X-rays from four categories (normal, COVID-19,
bacterial, and viral pneumonia). They used pre-trained transfer models based
on deep convolution neural networks, which resulted in a high-accuracy
decision assistance system for radiologists. A five-fold cross-validation
procedure was applied to obtain a robust result. 80% of the data was set aside
for training, while the remaining 20% was set aside for testing. With 96.1%,
ResNet50 and ResNet101 had the best overall performance.

COVID-Net, a deep convolutional neural network design for detecting COVID-19
instances from chest X-ray (CXR) pictures, was introduced by [118]. COVIDx is
an open-access benchmark dataset that contains 13,975 CXR images. [137]
employed a two-step transfer learning pipeline/strategy and the deep neural
network architecture COVID19XrayNet to detect COVID-19 with only 189 annotated
COVID-19 X-ray images. This COVID19XrayNet framework is created using a
modified ResNet34 structure that outperforms the original ResNet34 design.

[33] attempted to determine how Bayesian Convolutional Neural Networks
(Dropweights based) Networks (BCNN) can calculate uncertainty in DL
resolutions to improve the diagnostic accuracy of the human-machine
combination using COVID-19 chest X-ray in another paper. Their primary goal is
to avoid false-negative detection.

Author combined computational pharmacology, data mining, systems biology, and
computational chemistry to find important targets, affected networks, and
pharmaceutical intervention possibilities [53]. The findings suggested
pharmaceutical techniques consider therapeutic targets and networks. Data
mining can reveal regulatory trends, identify novel targets, and flag
undesirable impacts.

###  4.2 Long Short-Term Memory Networks

[100] proposed a DL-based quantitative CT model that incorporated five
clinico-radiological factors. They considered it as more efficient tool than
PSI and individual quantitative CT parameters for predicting the severity of
COVID-19 patients. The selection operator LASSO logistic regression analysis
is used to characterize the baseline of clinical-radiological features. In one
study, [1] proposed three hybrid methods for predicting the number of COVID-19
daily infected cases by combining different DL models, such as CNN-based
method, multi-head attention-based method, and LSTM-based method with the
Bayesian Optimization Algorithm. Using laboratory data, [5] developed a
clinical predictive model to determine whether a patient could have Covid-19
illness. For model evaluation, they calculated precision, F1-score, recall,
AUC, and accuracy ratings. Then, they put the model to the test using data
from 600 patients in 18 labs. They next used 10-fold cross-validation and
train-test split techniques to confirm it. They looked at samples from
Israel’s Albert Einstein Hospital. The authors trained six models: ANN, CNN,
LSTM, RNN, CNNLSTM, CNNRNN, and two hybrid models: CNNLSTM and CNNRNN.

[25] attempted to compare prediction models such as ARIMA, LSTM, Stacked LSTM,
and Prophet techniques to forecast the spread of COVID-19 in terms of infected
individuals, deaths, and recovered cases. They compared the forecasts’
accuracy and picked the best model based on the various performance
measurements and statistical hypothesis analysis. [51] used DL models to
forecast the number of Coronavirus positive reported cases for 32 states and
union territories in India. To forecast COVID-19 positive cases,

Table 1: Commonly Used Dataset for Covid-19 Research Serial No. |  Name |  Reference |  Citations |  Optimal Techniques
---|---|---|---|---
1 |  Cohen Dataset |  [20] |  491 |  Local PatchBased Method, InstaCovNet19, VGG16, DenseNet-121
2 |  - |  [123] |  1374 |  VGG16, ResNet-34, fficientNet-B0, ResNet50
3 |  JSRT Dataset |  [101] |  620 |  Local PatchBased Method [69], Polynomial Regression [82]
4 |  SCR Database |  [115] |  472 |  Local PatchBased Method [69]
5 |  MC Dataset |  [48] |  311 |  Local PatchBased Method
6 |  JHU CSSE Repository |  - |  - |  KNN, SVM, DTs, Holt winter [89], MLP [17]
7 |  Covid-19 Radiography Database |  [19] |  197 |  [89], [92], 24-Layer CNN [67], [116]
8 |  - |  [85] |  1064 |  Inception with Fractional-order [92]
9 |  Corona Hack: Chest X-Ray Dataset |  - |  - |  Local PatchBased Method [69]
10 |  Pneumonia Dataset |  - |  153 |  DenseNet-121 [86], [97], ResNet18 [57]
11 |  COVIDx Dataset |  [118] |  661 |  COVID-Net
12 |  Dr. Adrian Rosebrock‚ Äôs Dataset |  [46] |  - |  DenseNet201 [46]
13 |  RSNA Pneumonia Detection Dataset |  [120] |  - |  ResNet-101, ResNet-152, DenseNet201, Resnet50-V2, Inception-v3
14 |  Chest Imaging on Twitter |  [22] |  - |  DenseNet201, Resnet50-V2, Inception-v3
15 |  COVID-19 Chest X-ray Dataset Initiative |  [22] |  - |  DenseNet201, Resnet50-V2, Inception-v3

they used RNN-based LSTM variations such as Deep LSTM, Convolutional LSTM, and
Bi-directorial LSTM. The Convolutional LSTM model outperformed the other two
models in the [99] study, with error rates ranging from 2.0 to 3.3 percent for
all four datasets. Table. LABEL:Machine_learning_table reflects some
significant points of our work related to ML in COVID-19 patient detection.
The parameters that Long Short-Term Memory Networks offer include learning
rates, input and output biases, and a wide variety of others. It is
substantially faster at training and generates a lot more successful runs.

###  4.3 Convolutional Neural Networks

We categorize this subsection into three main parts. They are large scale
networks, medium scale networks, and small scale networks. Each of them is
again described in some smaller groups.

####  4.3.1 Large Scale Network

Due to the following epidemic, compiling a significant dataset to train a deep
neural network has proven problematic. [94] sought to develop a DL model for
detecting Coronavirus pneumonia on high-resolution X-rays, alleviating
radiologists’ workload and contributing to the epidemic’s control. They
defined the proposed solution as selected convolutional network architecture
and examine the design decisions, evaluation methodologies, and implementation
issues that go along with it. [132] propose a deep learning-based decision-
tree classifier for quickly recognizing COVID-19 from CXR pictures to make
quick decisions about Covid patients. Three binary decision trees are used in
their suggested classifier. In a paper, [47] have proposed a novel CNN model
which is based on 22-layer CNN architecture for the automatic detection of the
virus as well as to find out the accurate diagnosis of two (COVID and Normal),
three (COVID, Normal, and non-COVID pneumonia) and four (COVID, Normal, non-
COVID viral pneumonia, and non-COVID bacterial pneumonia) class
classification.

The main objective of the paper of [77] was to find out the effectiveness of
pre-trained CNN models for the detection of COVID-19 for rapid development.
Thus, the classification tasks were fine-tuned using three public chest X-ray
datasets and three pre-trained CNN models, namely SqueezeNet, AlexNet, and
GoogleNet, without any data augmentation techniques.

In [50], After pre-processing three CNN models are employed to examine and
compare their performance: ResNet, Inception V3, and Xception. Xception
outperforms all three models. To improve existing COVID-19 detection using a
limited number of publicly available CXR datasets, [133] designed and
developed a CXR-based COVID-19 disease detection and classification pipeline
using a modified VGG-16, ResNet50, and new EfficientNetB0 architecture in this
study.

####  4.3.2 Medium Scale Network

[37] proposed a DL-based Computer-aided diagnostic Framework (InstaCovNet-19)
to diagnose COVID-19 accurately and with lower misclassification rates. This
InstaCovNet-19 model is a DL algorithm built from various pre-trained models,
including ResNet-101, Inception v3, MobileNet, and NASNet.

Using an x-ray of the chest To detect the virus, [17] established a three-
stage model. After preprocessing, they employed five state-of-the-art transfer
learning models, AlexNet, MobileNetv2, ShuffleNet, SqueezeNet, and Xception,
along with three optimizers, Adam, SGDM, and RMSProp, to reduce over-fitting.
Finally, they have classified the data.

For the quick detection of the epidemic, [71] suggested a novel methodology
called FCOD, which was based on the Inception Network. They employed 17
Depthwise Separable Convolution Layers in this study, and their automatic
detection system can recognize complicated patterns from 2D X-ray images with
a short forecast time.

[92] proposed a hybrid classification system dubbed Inception Fractional-order
Marine Predators Algorithm, a blend of both Inception and Fractional-order
Marine Predators Algorithm (FO-MPA) (IFM). This proposed method successfully
extracted and selected features 136 for dataset one and 86 for dataset 2 out
of 51K features with high accuracy.

[43] developed a novel model incorporating preprocessing steps for the
detection of COVID-19 pneumonia infected cases in their work and provided a
quick decision-making tool for radiologists using computer-aided detection and
diagnosis (CAD) systems.

Table 2: Details of Papers that Accommodated Shallow Machine Learning Techniques References |  Input Type |  Method/ Technique Used |  No. of Image Data |  Augmentation Technique
---|---|---|---|---
|  |  |  |
[16] |  Text data |  Comprehensive ML |  777 |  NA
[141] |  Text data |  Gradient boosting machine |  51,831 individua-ls |  NA
[131] |  Clinical data |  SVM, LR, RF, KNN, AdaBoost |  137 |  NA
[12] |  CT |  SVM |  150 |  NA
[18] |  CT |  SVM |  326 |  NA
[134] |  Text data |  NLP, XGBoost, MLP, Bi-LSTM |  301,363 |  NA
[86] |  X-ray |  LR, PCA |  500 |  Scaling
[26] |  X-ray |  FrMEM, KNN |  2451 |  NA
[136] |  X-ray |  EfficientNet, ConfidNet |  519 |  NA
[20] |  X-ray |  NA |  679 |  NA
[62] |  Image data |  Resnet50 |  26140 |  NA
[65] |  Time series |  TodaYamamoto test, ML |  Annual Data |  NA
[55] |  Chest X-ray, CT |  VGGNet, NASNet |  254 |  NA
[97] |  Xray |  SVM, ResNet50, GLCM, AlexNet |  381 |  NA
[58] |  Text data |  TF/IDF, BOW, LR |  212 |  Preprocessing
[107] |  Time series data |  LR, MLP |  Not Defined |  NA
[74] |  Demographic and Clinical data |  SVM, MLP |  - |  NA
[110] |  Clinical data, CT |  Auto-ML |  319 |  NA
[89] |  Time series data |  ARIMA, SARIMA, GBR, RF, KNN |  - |  NA
[27] |  Text data |  DT, KNN, LR, LSVM, MNB, BNB |  7,486 |  Text Parsing, Data Cleaning
[124] |  CT |  AlexNet, UNet |  630 |  Rando -maffine trans -formation, color jittering
[17] |  Time series data |  MLP |  20706 Data Points |  Conversion of data
[130] |  CT image |  U-Net |  812 |  Gaussian blur
[141] |  RTPCR result |  Grad-Boost |  99,232 |  NA
[80] |  Text |  SVM |  2,670,000 |  NA
[45] |  EHR |  LR |  879 |  NA
[32] |  EHR |  ANN |  1040 |  NA
[100] |  CT image |  LR |  196 |  NA

Supervised learning models need massive data to train and generalize. Swish-
activated Residual U-Net GAN with dense blocks and skip connections. The
recommended GAN architecture handles unpredictable brightness and generates
realistic synthetic data due to instance normalization and sweep activation
[36].

Other Datasets and VGG: [34] proposed an ensemble model for the identification
of COVID-19 from infected, normal, and pneumonia people using two different
transfer learning models, VGG16 and DenseNet. They tested the suggested model
using two well-known datasets. For the early detection of COVID-19, [83] have
proposed a clinical decision support system that consists of three phases
based on the chest radiographic images. After the preprocessing and feature
extraction, classification and prediction are made with a fully connected
layer of several classifiers. Here, the VGG16 network is chosen to form this
model.

Using CT Scan Datasets and GoogleNet:

[139] provide an article for identifying Covid-19 using 3 popular deep
learning models named GoogleNet, ResNet, and AlexNet with transfer learning
technique. They used preprocessed lung CT images to create 2500 high-quality
pictures for training and validation.CNN’s deep learning technology could
revolutionize human life. [56]This study uses deep transfer learning-based CNN
to detect COVID-19 in chest CT images. Using VGG16, VGG19, Densenet121,
InceptionV3, Xception, and Resnet50. This could help clinicians and academics
build a therapy-decision tool.

####  4.3.3 Small Scale Network

Using X-rays Image Datasets and DenseNet121:

[122] presented a completely automatic innovative DL method to diagnose
Covid-19 and perform a prognosis analysis using bare chest pictures without
the use of human annotation in another publication. The planned DL system is
divided into three sections. First, they employed a completely automatic
Densenet121-FPN DL model for autonomous lung segmentation to avoid non-lung
areas. Second, a non-lung area suppression operation was proposed inside the
lung-ROI, with supplemental methods S4 employed to suppress non-lung area
intensities. Finally, they developed an auxiliary training procedure for pre-
training to allow the COVID-19Net to learn lung features from a vast dataset
using a DenseNet-Like structure.

Using CT Scan Datasets and DenseNet:

[52] in a paper, tried to evaluate and made compare all the eight pre-trained
models namely InceptionV3, NasNetMobile, ResNet50V2, DenseNet121, VGG19,
ResNet50, MobileNet, MobileNetV2 for the efficient classification of chest
X-ray images. DenseNet121 beats all other models to effectively classify
Normal, COVID-19, SARS, Bacterial Pneumonia, and Viral Pneumonia based on
chest X-ray pictures. [73] proposed a method called nCOVnet, a DL neural
network-based fast screening (in 5 seconds) approach for identifying COVID-19
by analyzing X-rays, which can address two drawbacks of high cost and limited
availability of testing kits.

Other Datasets and DenseNet:

[140] created a deep neural network architecture for feature selection that
consisted of 6 fully linked dense layers and 56 clinical variables as inputs.
The model prediction performance was utilized to rank the 56 features from the
neural network by importance after fitting using permutation importance
methods.

Using CT Scan Datasets and AlexNet:

[113] built an expert system based on chest X-ray images dubbed the
COVIDetectionNet model, achieving a 100% success rate for the detection using
pre-trained deep features ensemble as well as feature selection. Firstly, a
pre-trained AlexNet architecture is employed for feature extraction. Secondly,
the most efficient features are chosen using the Relief Algorithm based on the
K-Nearest Neighbor algorithm. Finally, these selected characteristics are
classified using the Support Vector Machine Method. [49] developed a two-stage
strategy model to detect the presence of COVID-19 while distinguishing
COVID-19 induced pneumonia from healthy, bacterial pneumonia, and viral
pneumonia cases in one study.

###  4.4 Graph Neural Networks

[57] recognized pneumonia chest x-ray using generative adversarial networks
(GAN). The efficiency of the suggested model was reflected in GAN, with
Resnet18 achieving the best accuracy. The proposed model is divided into three
stages and is based on two DL models.

[111] compare multiple time-series prediction techniques incorporating
auxiliary variables. Their Spatio-temporal graph neural networks approach
forecast the course of the pandemic.

[91] have proposed a Graph Isomorphic Network (GIN) based model named
GraphCovidNet to detect Coronavirus by using CT-scans, and CXRs of the Covid
affected patients.

By merging clinical, radiological, and biochemical data, [59] sought to
predict hospitalized patients’ outcomes. They used a DL model to assess CT
scan images and a radiologist report that included a semi-quantitative
description of CT scans to examine the increased amount of information offered
by CT scans.

[124] presented a weakly supervised 3D deep CNN dubbed DeCoVNet to Detect
COVID-19 in a study for COVID-19 classification and lesion localization. Using
the 2D UNet architecture, the first retrieved the 3D lung border of each chest
CT volume for lung area segmentation. DeCoVNet was built using medical ground
truth labels such as COVID-positive and COVID-negative. The DeCoVNet was
trained to generate predictions using the testing data. They got the affected
lesion localization result on the CAM activation region with the most overlap.

[121] attempted to develop an AI program that can effectively analyze
representative CT images to screen Covid-19 using DL Method by modifying the
typical Inception Network, based on the transfer learning neural network, and
fine-tuning the modified Inception (M-Inception) Model with pre-trained
weights. They used a fully linked layer to perform classification and
prediction.

[135] used DL-based software (uAI Intelligent Assistant Analysis System) to
aid in the diagnosis and quantification of COVID-19 Pneumonia. A modified 3D
convolution neural network and a combined V-Net with bottleneck structures are
used in this AI program.

###  4.5 Other DL Techniques

[126] presented DeepGLEAM. This coronavirus forecasting hybrid model beat
existing ML models, such as the vector auto-regressive (VAR) model and a pure
DL model, in one to four weeks forward prediction. They looked at approaches
to quantify uncertainty. DeepGLEAM outperformed the competition in terms of
accuracy and confidence intervals. This article [117] developed a visual
interpretative framework for understanding DL algorithms to diagnose the
epidemic. Their approach generates a comprehensive interpretation of the DL
model from various perspectives, including training patterns, diagnostic
efficiency, trained features, feature extraction, hidden nodes, diagnostic
prediction analysis, and so on.

[76] presented a framework for screening compound phenotype to determine the
application for Covid-19 patients using a DL-based DeepCE model. Besides using
a multiheaded attention mechanism they also suggest a new augmentation
technique for extracting meaningful information from configurations that
aren’t useful. They underscore the model’s importance by using it to re-
purpose the drugs for the virus.

[89] developed a potential model for measuring and the optimal lockdown
approach to alleviating the epidemic’s causalities in one study. The over-
fitting model authors studied three countries from each kind of type of
lockdown. [136] utilized chest X-rays to screen for viral Pneumonia using
CAAD, a machine-driven and accurate technique that relies less on labeled
viral pneumonia data. They wanted to improve the screening sensitivity by
predicting anomaly detection failures.

[110] proposed to develop and validate a Non-focus area prediction model in
the early stages of Coronavirus Pneumonia. They used the Auto-ML method to
excavate the texture features of the first chest CT image and assess the
model’s value in terms of the degree of Non-focus area damage and clinical
classification.

[90] investigated significant biases from data source variability to uncover
severity subgroups based on symptoms and comorbidities. They used the nCov2019
public dataset.

##  5 Critical Analysis on COVID-19 Research

###  5.1 Most common techniques acquired

This chapter discusses the most commonly used framework of ML and DL Model
techniques for COVID-19-related problems. An in-depth evaluation was
performed, and the purposes for using these specific methodologies as well as
their statistical data, are given in Fig. 10, Fig. 11 and Fig. 12.Among all
the papers we have reviewed, a significant percentage (60%) of paper that has
been used extensively is DL-based publications.

Figure 9: Chart demonstrating the usage percentage of CNN variants for
COVID-19 research

We noticed that many ML papers were used for resolving COVID-19 issues out of
all the documents included throughout this research study. We provided a
survey of ML models’ most used framework usage between them depending upon
those ML publications. Looking at Fig. 10, we can say that hybrid models (Deep
LSTM, Convolutional LSTM, Bi-Directional LSTM, CNN-LSTM, CNN RNN, IRRCNN,
BCNN), Decision Tree, SVM, RF and LR, XGBoost all these frameworks are
primarily used in 8% to 11.1% of the comprehensive paper analysis. Other than
utilizing these frameworks, SGD, NN, NLP, SVR, MLP, KNN, and other methods are
used for about 7.4% to 2.5% papers. These frameworks are used extensively for
classification and getting better and acceptable accuracy. Other ML methods,
such as Ensemble Algorithm, Catboost, GNN, Gradient boosting, Grad-CAM, and
SGD, were used in 1.2% of the total paper. They have been utilized for
statistical processing of COVID-19 data and also forecasting and predicting
COVID-19 time-series data.

Correspondingly, for the framework that has been used in DL methods, we
conducted an implementation review of the most commonly used techniques. A lot
of frameworks, such as DNN, CNN, RNN, GAN, LSTM, and so on, are widely
implemented, and the percentage of their usage is in Fig. 12. We found that
CNN’s are the most commonly implemented DL methods for overcoming COVID-19
based challenges. CNN is the crucial framework that has higher accomplishment
in the radiology, clinical imaging area. CNN is used widely in a lot of
papers, almost 31%. Moreover, we can see from Fig. 11 that LSTM was also
primarily used in papers which are known as one of the most efficient
approaches to DL algorithms for time series analysis of different data.

Based on our study of frameworks of CNN, one of the most diverse frameworks
that have been used in most of the papers, we discovered that AlexNet,
ResNet-34, ResNet50, MobileNet-V2, Inception V3, ResNet-18, U-Net, VGG-16,
DenseNet, FC-DenseNet103, DenseNet121, GoogleNet, ImageNet, InceptionV3,
Squeeze Net, and other models were used. From Fig. 9, ResNet algorithm is used
mostly in maximum papers. VGG is the second most used framework, and DenseNet,
AlexNet, XceptionNet CNN frameworks are most often employed for COVID-19 based
disputes.

###  5.2 Commonly used Data

With the new era of advancement, there is now tremendous change when we are
getting the authentic source of data, and now the collection of the dataset is
quite large. This continuous growth of medical images, it is quite difficult
to analyze by a medical expert, may have come more variations from different
expert and error-prone.

For automating the diagnosis process, alternative solution is using ML and DL
where these techniques have played an important role in medical image
segmentation, image classification, and computer-aided diagnosis. Based on
this scenario, we have calculated the percentage of a total of 100 papers.
After estimating percentage, it is noticed that image data is used in 63.6% of
papers, text data is used in 17% of papers, time-series data is used in 10.2%
of papers, EHR data used in 5.7% of paper, and other data forms are used in
3.4% of papers. The results of this is shown in Fig. 12.

###  5.3  Machine Learning in COVID-19 Prediction and Detection

According to the findings, computational ML, and DL algorithm techniques have
been effectively used in the classification of clinical images through factors
including the extraction of advanced features from spatial medical datasets
and the capacity to differentiate between Sars-Cov-2 and viral pneumonia. But
the situation of pandemic’s ultimate uncertainty will mislead the goal of
detection and prediction accuracy.

Table 3: Details of the Papers that accommodate Deep Learning Techniques References |  Input Type |  Method/ Technique Used |  Number of Images Data |  Augmentation Technique
---|---|---|---|---
[96] |  Chest X-Ray |  7 Depth wise separable convolutions layers |  940 |  Rotation, shifting, zooming, horizontal flipping
[68] |  Chest X-Ray |  Xception, ResNet50, MobileNet, InceptionV3 |  6249 |  NA
[50] |  Chest X-Ray |  InceptionV3, Xception, ResNet |  6432 |  Rotation, Zooming, Horizontal flipping
[69] |  Chest X-Ray |  FC-DenseNet103, ResNet-18 |  502 |  NA
[49] |  Chest X-Ray |  ResNet50, ResNet101 |  1215 |  Rotation, Gaussian blur
[122] |  CT Image |  DenseNet121 -FPN, COVID-19Net |  5372 |  NA
[8] |  CT Image |  EfficientNet B4 |  1186 |  lipping, scaling, rotation, brightness and blurring
[82] |  Timeseries Data |  SVR, PR, DNN, RNN, LSTM |  932,605 |  NA
[4] |  Chest X-Ray |  CNN |  1,389 |  Rotation, shear, zoom, width shift, height shift
[77] |  Chest X-Ray |  AlexNet, GoogleNet and SqueezeNet |  3666 |  NA
[127] |  CT Image |  Modified Inception transferlearning model |  1,065 |  NA
[39] |  Chest X-Ray |  Transfer learming with VGG16 and Resnet50 |  455 |  Horizontal flipping
[1] |  Time Series Data |  $ATT_{B}O$, $CNN_{B}O$, $LSTM_{B}O$ |  Not defined |  NA
[72] |  Chest X-ray, CTScan |  Transfer learning with VGG-19, Grad-CAM |  5856 |  NA
[37] |  Chest X-Ray |  ResNet101, Xception, NASNet, InceptionV3, MobileNetv2 with an Integrated stack technique |  1088 |  Shearing, zooming
[83] |  Chest Radio -graphic Image |  VGG16 |  300 |  Random flipping, scaling, shearing
[92] |  Chest X-Ray |  FO-MPA |  3435 |  NA
[47] |  CT Scan, Chest X-ray |  22-layer CNN architecture |  7390 |  NA
[34] |  Chest X-ray |  Ensemble model using VGG16 and Dense-Net |  - |  Horizontal flipping, vertical flipping, rotation and sheer transformation
[52] |  Chest X-ray |  VGG19, InceptionV3, ResNet50, ResNet50V2, MobileNet, MobileNetV2, DenseNet121, NasNetMobile |  760 |  NA
[137] |  Chest X-ray |  Transfer learning, ResNet34 |  189 |  Random vertical flip, resize crop, random horizontal flip and rotation
[113] |  Chest X-ray |  AlexNet, KNN, SVM |  6092 |  NA
[9] |  CT Image |  LSTM, MLP |  133 |  NA
[43] |  CXR Image |  VGG16 |  8,474 |  Shearing, zoom, rotation, width and height shift, and horizontal flip
[5] |  Text data |  ANN, CNN, LSTM, RNN, CNN -LSTM, CNN -RNN |  600 |  NA
[51] |  RTPCR |  Deep LSTM, Convolu -tional LSTM, Bi-direc -tional LSTM |  NA |  NA
[33] |  Chest X-ray |  BCNN, ResNet50V2 |  6009 |  Rotation, ZCA whitened, scaling, shifting, shearing, flipped horizontally and vertically
[99] |  Time series data |  Stacked LSTM, Bi -directioral LSTM, Convolu -tional LSTM, MAPE |  NA |  NA
[94] |  X-ray images |  X-ray images |  260 |  NA
[140] |  Text data |  Neural network predictive model |  220 |  NA
[28] |  Time series data |  LSTM, compared with ARIMA, NARANN |  9048 |  NA
[54] |  Chest X-ray |  cGAN, ResNet8, CNN |  1255 |  misa -ligned and slightly rotated
[29] |  CT images |  Inf-Net, SemiInf-Net, RA, FCN8s, U-Net |  1700 |  Resizing
[120] |  Chest X-ray |  ResNet-50, ResNet-101, ResNet-152 |  1853 |  NA
[2] |  Chest X-ray |  MobileNet V1, MobileNet V2, MobileNet V3, ShuffleNet-V1, SquezeNet, Dark COVID-Net, SPP-COVID-Net |  2686 |  NA
[30] |  Chest X-ray |  AlexNet, MobileNetv2, ShuffleNet, SqueezeNet, Xception |  148 |  NA
[108] |  CT images |  VB-Net, GGO, XGboost, AFSDF, LR, SVM, RF, NN, ElasticNet |  2522 |  NA
[98] |  CT scans |  Efficient Nets (B0-B5), NasNetLarge, NasNetMobile, InceptionV3, ResNet-50, SeResnet50, Xception, DenseNet121, ResNeXt50, Inception -ResNetV2 |  746 |  Horizontally and vertically shift, random rotation, flip horizontally
[84] |  CCTV image |  Image processing and CNN |  858 |  Resizing
[67] |  Chest CT, X-ray images |  CNN, GGO, Transfer learning |  4561 |  Resizing, Max pooling
[134] |  Text data |  NLP, XGBoost, MLP, Bi-LSTM, SVR |  301,363 |  NA
[95] |  CT scans |  FastCapsNet, CapsNet, ResNet50, AlexNet |  3000 |  NA
[22] |  X-Ray images |  DenseNet201, ResNet50V2, Inceptionv3 |  1006 |  Normalizing, resizing
[50] |  X-Ray images |  XCeption, InceptionNet3, ResNet, CNN |  6432 |  Scaling
[75] |  CT scan, CXR images |  Resnet50, VGG16, InceptionV3 |  5,232 |  Max pooling 2d
[30] |  X-ray images |  AlexNet, MobileNetv2, ShuffleNet, SqueezeNet, Xception |  148 |  NA
[66] |  X-ray images |  InceptionV3, ResNet50, ResNet101, ResNet152, InceptionResNetV2 |  7065 |  NA
[81] |  images |  ResNet-34, ResNet-50 |  1122 |  Rotating, zooming, height shifting
[35] |  CT Scan Images |  Resnet-50, Deep CNN |  6356 |  NA
[15] |  Chest X-rays |  VGG-16, Grad-CAM |  6,523 |  Average Pooling 2D, Flatten, Dense, Dropout, Dense
[60] |  CXR images |  FC-classifier, MobileNetV2, DenseNet-121, SqueezeNet |  537 |  NA
[64] |  NA |  Patho-biology of SARS-CoV-2 virus |  NA |  NA
[63] |  CT scans |  Xception, U-Net |  275 |  NA
[135] |  RT-PCR results, CT scans |  It is a software |  4675 |  NA
[11] |  CXR images |  (1) Sectra PACS (2) ITK -Snap |  86 |  AD segmentation
[33] |  X-ray images |  ResNet50V2 |  6009 |  Whitened, rotated, flipped horizontally, vertically, scaled, shifted, sheared
[57] |  X-ray images |  AlexNet, GoogleNet, SqueezNet, ResNet18 |  5863 |  NA
[118] |  CXR images |  VGG-19, ResNet-50 |  13,975 |  NA
[46] |  Texts, tweets |  GNN |  6460 |  Separating comma, other punctuations
[40] |  X-ray images |  VGG19, DenseNet, InceptionV3, ResNetV2, Xception, MobileNetV2, InceptionResNet-V2 |  50 |  NA
[112] |  Texts, articles |  ScispaCy NER model, BIONLP13CG corpus |  60,000 papers |  Semantic visualization techniques
[42] |  CT scans |  VGG-16 , ResNet-18 , ResNet-50, DenseNet-121, DenseNet-169, EfficientNetb0, EfficientNet-b1, CRNet |  746 |  NA
[73] |  X-ray images |  VGG16 Model |  6388 |  Rotation, flipped horizontally and vertically
[6] |  CT image, X-ray |  IRRCNN |  5216 |  Class specific data augmentation
[105] |  Images |  ResNet-50, DenseNet, VGG16 |  1990 |  Translational and rotational
[116] |  CXR images |  VGG16 |  1124 |  Synthetic augmentation
[132] |  CXR images |  ResNet18 |  1884 |  Horizontal flip, rotations, shifts
Table 3: Continued From previous page

Figure 10: The percentage ratio of different types of Machine learning methods
in COVID-19 research community

  * •

The exact amount of people affected died or still tested COVID-19 positive
cannot be predicted with a 100% accuracy because of the false-positive and
false-negative high ratings and less COVID-19 detection test.

Figure 11: The percentage ratio of different types of Deep learning methods in
COVID-19 research community Figure 12: The percentage of published COVID-19
papers with respect to the types of data used in their research

  * •

Proper annotation of images are also affecting the COVID-19 prediction using
ML and DL. As most of the detection and prediction is done by medical images,
annotation of the image dataset should be properly done.

  * •

The data that is currently available today is focused on COVID-19 tests that
are conducted differently in various countries. They may not have to represent
the whole viral infection in detail, which may be the ML and DL model’s
conceptual input. To accurately diagnose and detect this disease, and to do
proper forecasting using ML and DL methods we need to implement accurate data.

###  5.4 Impact of Data Augmentation

We can use data augmentation to get more variant data, prevent overfitting for
better prediction, and perfectly train the model. By using data augmentation,
it helps to increase the diversity of data as well as help to avoid
overfitting. It can be more helpful for small datasets by increasing the size
of data for better image segmentation, classification, and object detection.
Before applying data augmentation, we need to understand the problem domain.

Figure 13: Usage of types of images data that are acquired by our the paper
demonstrated in our survey(Merge Fig.12 and 13) Figure 14: Different types of
Data augmentation techniques applied by the research papers illustrated in our
survey

It can be applied as a pre-processing step before train the model after
estimating the percentage from Fig. 14, we can observe that powerful data
augmentation techniques are used in many articles. Here, Rotation, Flipping,
and Scaling have mainly used techniques. Also, there are having different data
augmentation techniques along with others. For developing a high-performance
model, data augmentation can be used as a powerful tool as well as need
rigorous problem analysis and domain knowledge.

##  6 Research Challenges

The following are the obstacles and issues related to DL implementations to
monitor the COVID-19 pandemic:

  * •

Class-imbalance Problem: As Covid-19 pandemic outbreaks in 2020, “data
collection” was an obvious challenge for the authors. They find very little
data on patients with the Coronavirus. On the other hand, vast data of
pneumonia patients or patients with other lung diseases and healthy patients
cause a class imbalance problem. To deal with this issue, [136] suggested an
anomaly detector instead of a classifier as the classifiers yield poor
sensitivity performance for the class imbalance problem.

  * •

Limited Data and Limitation Labeled Data: Most of the papers published in 2020
faced this problem of limited data of COVID 19. They were trying to collect
more data not only with quality but also with labeling and annotation.

  * •

Lack of Data Quality: Some of them collected data from online stores (books or
pdf or newspapers) or from some random collection which impacts the quality of
the research. Data should be collected from authentic sources like hospitals,
clinics, or other research papers. Good quality data can make the analysis
more structured. [116] faced a challenge to create high-resolution samples
from highly variable data sets. [73] have used only Posterior Anterior as a
dataset.

  * •

Selection Bias: During collecting data, considering 4g different variables is
a must to enhance the quality of the data. Such as [20] did not consider
immunosuppression status or ethnic background. For the primary research, it is
a challenge to contemplate so many variables. Further research that is
continuous analysis will improve this condition. Selection bias is also seen
while gathering publicly available images.

  * •

Less Number of Features: Some of the papers demand that their work would be
more efficient if the number of features were increased. They will cope up
with this challenge in the further experiments they require.

  * •

Single-center Design: Single-center data impacts hugely in the testing. The
result may vary in the large dataset or a multi-centered dataset. Due to the
single-center design, their dataset is also smaller in size. [11] and [135]
found it difficult to collect multi-centered data. They felt that there was a
need to do in-depth research to make this more potential.

  * •

Dataset on X-ray, not CT image: Comparing with computed tomography (CT), chest
X-ray cannot come up with 3D anatomy and is less accurate than a PCR
diagnostic. Chest Computed Tomography (CT) is a captivating option for patient
management due to its spectacular portability, low cost, availability, and
scalability. It plays an essential role in identifying earlier lung infections
and monitoring disease development.

  * •

Unbalanced Data: For improved training in DL models and to obtain better
features, we need a vast amount of balanced dataset. Due to this problem, the
authors encountered many hurdles and had to apply different strategies to make
it balanced, which is time-consuming.

  * •

Hardware Limitations: Extensive data need for training intense models in less
amount of time. Accordingly, we need more powerful GPUs and high computing
power.

  * •

Lack of Diverse Datasets: Due to the lack of a diverse dataset, it is hard to
analyze and perfectly learn the models. When the model is fed with various
training data, it provides more discriminative information, making the model
unique.

  * •

Security of Privacy: By preserving privacy and maintaining the security of
patient’s medical information, it is hard to collect a large number of medical
images, which hinders the training and validation of models.

  * •

Time-intensive and Costly: High-quality data preparation is needed for the
fast detection of COVID-19 when there are hidden biases in the dataset.
Typically it requires time-intensive, expensive human labor.

##  7 Future Research Directions

For fighting against the COVID-19, the new revolution of technology can be the
savior of our lives. Future COVID-19 research directions will guide healthcare
planning, telemedicine, management strategies, and robust disease prediction
at the earliest stage. In this regard, AI-based ML and DL systems contribute
to the available resources where Transfer Learning plays a significant role
with the help of unbalanced and limited datasets.

  * •

Robust Development Architecture for COVID-19 and other Diseases: An automated,
robust architecture can be built with the help of image classification using
X-ray and CT images for the detection of COVID-19 as well as other diseases,
which will provide an end-to-end solution for fast detection.

  * •

Necessity of Combined Data Repository: For further research development work,
we need more data to combat the COVID-19 situation. Besides, this Combined
Data Repository will help us to fight back with similar pandemics.

  * •

Implementation of Medical Assistive in Real-World: For supporting the health
professionals, it is mandatory to implement this medical-assisting tool in
hospitals, radiology clinics.

  * •

Intelligent Robot: For assisting the COVID-19 infected patients without human
labor, these robots are supposed to be used in hospitals to curb the spread of
disease.

  * •

Coronavirus detection by checking breathing sound: This can be a new and
easier area for detecting coronavirus. Scientists can focus on this for fast
detection.

  * •

AI in Biological Research: For biological research, this AI system can be used
to predict the structure of proteins and genetic sequence.

It can be stated that more future direction and research are required for
efficient identification and diagnosis.

## Appendixes

Remainder omitted in this sample. See http://www.jmlr.org/papers/ for full
paper.

## References

  * [1] Hossein Abbasimehr and Reza Paki.  Prediction of covid-19 confirmed cases combining deep learning methods and bayesian optimization.  Chaos, Solitons & Fractals, 142:110511, 2021.
  * [2] Siti Raihanah Abdani, Mohd Asyraf Zulkifley, and Nuraisyah Hani Zulkifley.  A lightweight deep learning model for covid-19 detection.  In 2020 IEEE Symposium on Industrial Electronics & Applications (ISIEA), pages 1–5. IEEE, 2020.
  * [3] Parnian Afshar, Shahin Heidarian, Farnoosh Naderkhani, Anastasia Oikonomou, Konstantinos N Plataniotis, and Arash Mohammadi.  Covid-caps: A capsule network-based framework for identification of covid-19 cases from x-ray images.  Pattern Recognition Letters, 138:638–643, 2020.
  * [4] Faizan Ahmed, Syed Ahmad Chan Bukhari, and Fazel Keshtkar.  A deep learning approach for covid-19 8 viral pneumonia screening with x-ray images.  Digital Government: Research and Practice, 2(2):1–12, 2021.
  * [5] Talha Burak Alakus and Ibrahim Turkoglu.  Comparison of deep learning approaches to predict covid-19 infection.  Chaos, Solitons & Fractals, 140:110120, 2020.
  * [6] Md Zahangir Alom, M M Shaifur Rahman, Mst Shamima Nasrin, Tarek M. Taha, and Vijayan K. Asari.  Covid_mtnet: Covid-19 detection with multi-task deep learning approaches, 2020.
  * [7] Dilip Kumar Bagal, Arati Rath, Abhishek Barua, and Dulu Patnaik.  Estimating the parameters of susceptible-infected-recovered model of covid-19 cases in india during lockdown periods.  Chaos, Solitons & Fractals, 140:110154, 2020.
  * [8] Harrison X Bai, Robin Wang, Zeng Xiong, Ben Hsieh, Ken Chang, Kasey Halsey, Thi My Linh Tran, Ji Whae Choi, Dong-Cui Wang, Lin-Bo Shi, et al.  Artificial intelligence augmentation of radiologist performance in distinguishing covid-19 from pneumonia of other origin at chest ct.  Radiology, 296,sahlol2020covid(3):E156–E165, 2020.
  * [9] Xiang Bai, Cong Fang, Yu Zhou, Song Bai, Zaiyi Liu, Liming Xia, Qianlan Chen, Yongchao Xu, Tian Xia, Shi Gong, et al.  Predicting covid-19 malignant progression with ai techniques.  ABC, 2020.
  * [10] Pedro Baqui, Ioana Bica, Valerio Marra, Ari Ercole, and Mihaela van Der Schaar.  Ethnic and regional variations in hospital mortality from covid-19 in brazil: a cross-sectional observational study.  The Lancet Global Health, 8(8):e1018–e1026, 2020.
  * [11] Eduardo Mortani Barbosa Jr, Warren B Gefter, Rochelle Yang, Florin C Ghesu, Siqi Liu, Boris Mailhe, Awais Mansoor, Sasa Grbic, Sebastian Piat, Guillaume Chabin, et al.  Automated detection and quantification of covid-19 airspace disease on chest radiographs: A novel approach achieving radiologist-level performance using a cnn trained on digital reconstructed radiographs (drrs) from ct-based ground-truth.  arXiv preprint arXiv:2008.06330, 2020.
  * [12] Mucahid Barstugan, Umut Ozkaya, and Saban Ozturk.  Coronavirus (covid-19) classification using ct images by machine learning methods.  arXiv preprint arXiv:2003.09424, 2020.
  * [13] Muhammad Farhan Bashir, Benjiang Ma, Bushra Komal, Muhammad Adnan Bashir, Duojiao Tan, Madiha Bashir, et al.  Correlation between climate indicators and covid-19 pandemic in new york, usa.  Science of the Total Environment, 728:138835, 2020.
  * [14] Vaibhav Bhatnagar, Ramesh Chandra Poonia, Pankaj Nagar, Sandeep Kumar, Vijander Singh, Linesh Raja, and Pranav Dass.  Descriptive analysis of covid-19 patients in the context of india.  Journal of Interdisciplinary Mathematics, pages 1–16, 2020.
  * [15] Luca Brunese, Francesco Mercaldo, Alfonso Reginelli, and Antonella Santone.  Explainable deep learning for pulmonary disease and coronavirus covid-19 detection from x-rays.  Computer Methods and Programs in Biomedicine, 196:105608, 2020.
  * [16] María A Callejon-Leblic, Ramon Moreno-Luna, Alfonso Del Cuvillo, Isabel M Reyes-Tejero, Miguel A Garcia-Villaran, Marta Santos-Peña, Juan M Maza-Solano, Daniel I Martín-Jimenez, Jose M Palacios-Garcia, Carlos Fernandez-Velez, et al.  Loss of smell and taste can accurately predict covid-19 infection: A machine-learning approach.  Journal of Clinical Medicine, 10(4):570, 2021.
  * [17] Zlatan Car, Sandi Baressi Šegota, Nikola Anđelić, Ivan Lorencin, and Vedran Mrzljak.  Modeling the spread of covid-19 infection using a multilayer perceptron.  Computational and mathematical methods in medicine, 2020, 2020.
  * [18] Hui Juan Chen, Li Mao, Yang Chen, Li Yuan, Fei Wang, Xiuli Li, Qinlei Cai, Jie Qiu, Jie Tian, and Feng Chen.  Machine learning-based ct radiomics model distinguishes covid-19 from non-covid-19 pneumonia.  ABC, 2021.
  * [19] Muhammad EH Chowdhury, Tawsifur Rahman, Amith Khandakar, Rashid Mazhar, Muhammad Abdul Kadir, Zaid Bin Mahbub, Khandakar Reajul Islam, Muhammad Salman Khan, Atif Iqbal, Nasser Al Emadi, et al.  Can ai help in screening viral and covid-19 pneumonia?  IEEE Access, 8:132665–132676, 2020.
  * [20] Joseph Paul Cohen, Paul Morrison, Lan Dao, Karsten Roth, Tim Q Duong, and Marzyeh Ghassemi.  Covid-19 image data collection: Prospective predictions are the future.  arXiv preprint arXiv:2006.11988, 2020.
  * [21] Wesley Cota et al.  Monitoring the number of covid-19 cases and deaths in brazil at municipal and federative units level.  ABC, 2020.
  * [22] Amit Kumar Das, Sayantani Ghosh, Samiruddin Thunder, Rohit Dutta, Sachin Agarwal, and Amlan Chakrabarti.  Automatic covid-19 detection from x-ray images using ensemble learning with convolutional neural network.  Pattern Analysis and Applications, pages 1–14, 2021.
  * [23] Tiago de Melo and Carlos MS Figueiredo.  A first public dataset from brazilian twitter and news on covid-19 in portuguese.  Data in brief, 32:106179, 2020.
  * [24] Andreza Aruska de Souza Santos, Darlan da Silva Candido, William Marciel de Souza, Lewis Buss, Sabrina L Li, Rafael HM Pereira, Chieh-Hsi Wu, Ester C Sabino, and Nuno R Faria.  Dataset on sars-cov-2 non-pharmaceutical interventions in brazilian municipalities.  Scientific data, 8(1):1–6, 2021.
  * [25] Jayanthi Devaraj, Rajvikram Madurai Elavarasan, Rishi Pugazhendhi, GM Shafiullah, Sumathi Ganesan, Ajay Kaarthic Jeysree, Irfan Ahmad Khan, and Eklas Hossain.  Forecasting of covid-19 cases using deep learning models: Is it reliable and practically significant?  Results in Physics, 21:103817, 2021.
  * [26] Mohamed Abd Elaziz, Khalid M Hosny, Ahmad Salah, Mohamed M Darwish, Songfeng Lu, and Ahmed T Sahlol.  New machine learning method for image-based diagnosis of covid-19.  Plos one, 15(6):e0235187, 2020.
  * [27] Mohamed K Elhadad, Kin Fun Li, and Fayez Gebali.  Detecting misleading information on covid-19.  Ieee Access, 8:165201–165215, 2020.
  * [28] Ammar H Elsheikh, Amal I Saba, Mohamed Abd Elaziz, Songfeng Lu, S Shanmugan, T Muthuramalingam, Ravinder Kumar, Ahmed O Mosleh, FA Essa, and Taher A Shehabeldeen.  Deep learning-based forecasting model for covid-19 outbreak in saudi arabia.  Process Safety and Environmental Protection, 149:223–233, 2021\.
  * [29] Deng-Ping Fan, Tao Zhou, Ge-Peng Ji, Yi Zhou, Geng Chen, Huazhu Fu, Jianbing Shen, and Ling Shao.  Inf-net: Automatic covid-19 lung infection segmentation from ct images.  IEEE Transactions on Medical Imaging, 39(8):2626–2637, 2020.
  * [30] Zeming Fan, Mudasir Jamil, Muhammad Tariq Sadiq, Xiwei Huang, and Xiaojun Yu.  Exploiting multiple optimizers with transfer learning techniques for the identification of covid-19 patients.  Journal of healthcare engineering, 2020, 2020.
  * [31] Muhammad Farooq and Abdul Hafeez.  Covid-resnet: A deep learning framework for screening of covid19 from radiographs.  arXiv preprint arXiv:2003.14395, 2020.
  * [32] Fernando Timoteo Fernandes, Tiago Almeida de Oliveira, Cristiane Esteves Teixeira, Andre Filipe de Moraes Batista, Gabriel Dalla Costa, and Alexandre Dias Porto Chiavegatto Filho.  A multipurpose machine learning approach to predict covid-19 negative prognosis in são paulo, brazil.  Scientific reports, 11(1):1–7, 2021.
  * [33] Biraja Ghoshal and Allan Tucker.  Estimating uncertainty and interpretability in deep learning for coronavirus (covid-19) detection.  arXiv preprint arXiv:2003.10769, 2020.
  * [34] Neha Gianchandani, Aayush Jaiswal, Dilbag Singh, Vijay Kumar, and Manjit Kaur.  Rapid covid-19 diagnosis using ensemble deep transfer learning models from chest radiographic images.  Journal of ambient intelligence and humanized computing, pages 1–13, 2020.
  * [35] Ophir Gozes, Maayan Frid-Adar, Hayit Greenspan, Patrick D Browning, Huangqi Zhang, Wenbin Ji, Adam Bernheim, and Eliot Siegel.  Rapid ai development cycle for the coronavirus (covid-19) pandemic: Initial results for automated detection & patient monitoring using deep learning ct image analysis.  arXiv preprint arXiv:2003.05037, 2020.
  * [36] Rutwik Gulakala, Bernd Markert, and Marcus Stoffel.  Generative adversarial network based data augmentation for cnn based detection of covid-19.  Scientific Reports, 12(1):1–15, 2022.
  * [37] Anunay Gupta, Shreyansh Gupta, Rahul Katarya, et al.  Instacovnet-19: A deep learning classification model for the detection of covid-19 patients using chest x-ray.  Applied Soft Computing, 99:106859, 2021.
  * [38] Rajan Gupta and Saibal Kumar Pal.  Trend analysis and forecasting of covid-19 outbreak in india.  MedRxiv, 2020.
  * [39] Lawrence O Hall, Rahul Paul, Dmitry B Goldgof, and Gregory M Goldgof.  Finding covid-19 from chest x-rays using deep learning on a small dataset.  arXiv preprint arXiv:2004.02060, 2020.
  * [40] Abdullah Hamid, Nasrullah Shiekh, Naina Said, Kashif Ahmad, Asma Gul, Laiq Hassan, and Ala Al-Fuqaha.  Fake news detection in social media using graph neural networks and nlp techniques: A covid-19 use-case.  arXiv preprint arXiv:2012.07517, 2020.
  * [41] Arezoo Haratian, Hadi Fazelinia, Zeinab Maleki, Hao Wang, Mark Lewis, Russell Greiner, David Wishart, et al.  Dataset of covid-19 outbreak and potential predictive features in the usa.  medRxiv, 2020.
  * [42] Xuehai He, Xingyi Yang, Shanghang Zhang, Jinyu Zhao, Yichen Zhang, Eric Xing, and Pengtao Xie.  Sample-efficient deep learning for covid-19 diagnosis based on ct scans.  MedRxiv, 2020.
  * [43] Morteza Heidari, Seyedehnafiseh Mirniaharikandehei, Abolfazl Zargari Khuzani, Gopichandh Danala, Yuchen Qiu, and Bin Zheng.  Detecting covid-19 infected pneumonia from x-ray images using a deep learning model with image preprocessing algorithm.  In Medical Imaging 2021: Computer-Aided Diagnosis, volume 11597, page 115970V. International Society for Optics and Photonics, 2021.
  * [44] Shahin Heidarian, Parnian Afshar, Nastaran Enshaei, Farnoosh Naderkhani, Moezedin Javad Rafiee, Faranak Babaki Fard, Kaveh Samimi, S Farokh Atashzar, Anastasia Oikonomou, Konstantinos N Plataniotis, et al.  Covid-fact: A fully-automated capsule network-based framework for identification of covid-19 cases from chest ct scans.  Frontiers in Artificial Intelligence, 4, 2021.
  * [45] Frank S Heldt, Marcela P Vizcaychipi, Sophie Peacock, Mattia Cinelli, Lachlan McLachlan, Fernando Andreotti, Stojan Jovanović, Robert Dürichen, Nadezda Lipunova, Robert A Fletcher, et al.  Early risk assessment for covid-19 patients from emergency department data using machine learning.  Scientific reports, 11(1):1–13, 2021.
  * [46] Ezz El-Din Hemdan, Marwa A Shouman, and Mohamed Esmail Karar.  Covidx-net: A framework of deep learning classifiers to diagnose covid-19 in x-ray images.  arXiv preprint arXiv:2003.11055, 2020.
  * [47] Emtiaz Hussain, Mahmudul Hasan, Md Anisur Rahman, Ickjai Lee, Tasmi Tamanna, and Mohammad Zavid Parvez.  Corodet: A deep learning based classification for covid-19 detection using chest x-ray images.  Chaos, Solitons & Fractals, 142:110495, 2021.
  * [48] Stefan Jaeger, Sema Candemir, Sameer Antani, Yì-Xiáng J Wáng, Pu-Xuan Lu, and George Thoma.  Two public chest x-ray datasets for computer-aided screening of pulmonary diseases.  Quantitative imaging in medicine and surgery, 4(6):475, 2014.
  * [49] Govardhan Jain, Deepti Mittal, Daksh Thakur, and Madhup K Mittal.  A deep learning approach to detect covid-19 coronavirus with x-ray images.  Biocybernetics and biomedical engineering, 40(4):1391–1405, 2020\.
  * [50] Rachna Jain, Meenu Gupta, Soham Taneja, and D Jude Hemanth.  Deep learning based detection and analysis of covid-19 on chest x-ray images.  Applied Intelligence, 51(3):1690–1700, 2021.
  * [51] Mohammad Jamshidi, Ali Lalbakhsh, Jakub Talla, Zdeněk Peroutka, Farimah Hadjilooei, Pedram Lalbakhsh, Morteza Jamshidi, Luigi La Spada, Mirhamed Mirmozafari, Mojgan Dehghani, et al.  Artificial intelligence and covid-19: deep learning approaches for diagnosis and treatment.  IEEE Access, 8:109581–109595, 2020.
  * [52] KC Kamal, Zhendong Yin, Mingyang Wu, and Zhilu Wu.  Evaluation of deep learning-based approaches for covid-19 classification based on chest x-ray images.  Signal, Image and Video Processing, pages 1–8, 2021.
  * [53] Austė Kanapeckaitė, Asta Mažeikienė, Liesbet Geris, Neringa Burokienė, Graeme S. Cottrell, and Darius Widera.  Computational pharmacology: New avenues for covid-19 therapeutics search and better preparedness for future pandemic crises.  Biophysical Chemistry, 290:106891, 2022.
  * [54] Stefanos Karakanis and Georgios Leontidis.  Lightweight deep learning models for detecting covid-19 from chest x-ray images.  Computers in biology and medicine, 130:104181, 2021.
  * [55] Sara Hosseinzadeh Kassani, Peyman Hosseinzadeh Kassasni, Michal J Wesolowski, Kevin A Schneider, and Ralph Deters.  Automatic detection of coronavirus disease (covid-19) in x-ray and ct images: A machine learning-based approach.  arXiv preprint arXiv:2004.10641, 2020.
  * [56] Nirmala Devi Kathamuthu, Shanthi Subramaniam, Quynh Hoang Le, Suresh Muthusamy, Hitesh Panchal, Suma Christal Mary Sundararajan, Ali Jawad Alrubaie, and Musaddak Maher Abdul Zahra.  A deep transfer learning-based convolution neural network model for covid-19 detection using computed tomography scan images for medical applications.  Advances in Engineering Software, 175:103317, 2023.
  * [57] Nour Eldeen M Khalifa, Mohamed Hamed N Taha, Aboul Ella Hassanien, and Sally Elghamrawy.  Detection of coronavirus (covid-19) associated pneumonia based on generative adversarial networks and a fine-tuned deep transfer learning model using chest x-ray dataset.  arXiv preprint arXiv:2004.01184, 2020.
  * [58] Akib Mohi Ud Din Khanday, Syed Tanzeel Rabani, Qamar Rayees Khan, Nusrat Rouf, and Masarat Mohi Ud Din.  Machine learning based approaches for detecting covid-19 using clinical text data.  International Journal of Information Technology, 12(3):731–739, 2020.
  * [59] Nathalie Lassau, Samy Ammari, Emilie Chouzenoux, Hugo Gortais, Paul Herent, Matthieu Devilder, Samer Soliman, Olivier Meyrignac, Marie-Pauline Talabard, Jean-Philippe Lamarque, et al.  Integrating deep learning ct-scan model, biological and clinical variables to predict severity of covid-19 patients.  Nature communications, 12(1):1–11, 2021.
  * [60] Xin Li, Chengyin Li, and Dongxiao Zhu.  Covid-mobilexpert: On-device covid-19 patient triage and follow-up using chest x-rays.  In 2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pages 1063–1067. IEEE, 2020.
  * [61] Yen-Chin Liu, Rei-Lin Kuo, and Shin-Ru Shih.  Covid-19: The first documented coronavirus pandemic in history.  Biomedical journal, 43(4):328–333, 2020.
  * [62] Mohamed Loey, Gunasekaran Manogaran, Mohamed Hamed N Taha, and Nour Eldeen M Khalifa.  A hybrid deep transfer learning model with machine learning methods for face mask detection in the era of the covid-19 pandemic.  Measurement, 167:108288, 2021.
  * [63] Rohit Lokwani, Ashrika Gaikwad, Viraj Kulkarni, Aniruddha Pant, and Amit Kharat.  Automated detection of covid-19 from ct scans using convolutional neural networks.  arXiv preprint arXiv:2006.13212, 2020.
  * [64] Robert J Mason.  Pathogenesis of covid-19 from a cell biology perspective, 2020.
  * [65] Marco Mele and Cosimo Magazzino.  Pollution, economic growth, and covid-19 deaths in india: a machine learning evidence.  Environmental Science and Pollution Research, 28(3):2669–2677, 2021\.
  * [66] Ali Narin, Ceren Kaya, and Ziynet Pamuk.  Automatic detection of coronavirus disease (covid-19) using x-ray images and deep convolutional neural networks.  arXiv preprint arXiv:2003.10849, 2020.
  * [67] Malaya Kumar Nath, Aniruddha Kanhe, and Madhusudhan Mishra.  A novel deep learning approach for classification of covid-19 images.  In 2020 IEEE 5th International Conference on Computing Communication and Automation (ICCCA), pages 752–757. IEEE, 2020.
  * [68] Soumya Ranjan Nayak, Deepak Ranjan Nayak, Utkarsh Sinha, Vaibhav Arora, and Ram Bilas Pachori.  Application of deep learning techniques for detection of covid-19 cases using chest x-ray images: A comprehensive study.  Biomedical Signal Processing and Control, 64:102365, 2021.
  * [69] Yujin Oh, Sangjoon Park, and Jong Chul Ye.  Deep learning covid-19 features on cxr using limited training data sets.  IEEE Transactions on Medical Imaging, 39(8):2688–2700, 2020.
  * [70] Coşku Öksüz, Oğuzhan Urhan, and Mehmet Kemal Güllü.  Ensemble-cvdnet: A deep learning based end-to-end classification framework for covid-19 detection using ensembles of networks.  arXiv preprint arXiv:2012.09132, 2020.
  * [71] Amir Hossein Panahi, Alireza Rafiei, and Alireza Rezaee.  Fcod: Fast covid-19 detector based on deep learning techniques.  Informatics in medicine unlocked, 22:100506, 2021.
  * [72] Harsh Panwar, PK Gupta, Mohammad Khubeb Siddiqui, Ruben Morales-Menendez, Prakhar Bhardwaj, and Vaishnavi Singh.  A deep learning and grad-cam based color visualization approach for fast detection of covid-19 cases using chest x-ray and ct-scan images.  Chaos, Solitons & Fractals, 140:110190, 2020.
  * [73] Harsh Panwar, PK Gupta, Mohammad Khubeb Siddiqui, Ruben Morales-Menendez, and Vaishnavi Singh.  Application of deep learning for fast detection of covid-19 in x-rays using ncovnet.  Chaos, Solitons & Fractals, 138:109944, 2020.
  * [74] Dhruv Patel, Vikram Kher, Bhushan Desai, Xiaomeng Lei, Steven Cen, Neha Nanda, Ali Gholamrezanezhad, Vinay Duddalwar, Bino Varghese, and Assad A Oberai.  Machine learning based predictors for covid-19 disease severity.  Scientific Reports, 11(1):1–7, 2021.
  * [75] Varalakshmi Perumal, Vasumathi Narayanan, and Sakthi Jaya Sundar Rajasekar.  Detection of covid-19 using cxr and ct images using transfer learning and haralick features.  Applied Intelligence, 51(1):341–358, 2021.
  * [76] Thai-Hoang Pham, Yue Qiu, Jucheng Zeng, Lei Xie, and Ping Zhang.  A deep learning framework for high-throughput mechanism-driven phenotype compound screening and its application to covid-19 drug repurposing.  Nature Machine Intelligence, 3(3):247–257, 2021.
  * [77] Tuan D Pham.  Classification of covid-19 chest x-rays with deep learning: new models or fine tuning?  Health Information Science and Systems, 9(1):1–11, 2021.
  * [78] Lan T Phan, Thuong V Nguyen, Quang C Luong, Thinh V Nguyen, Hieu T Nguyen, Hung Q Le, Thuc T Nguyen, Thang M Cao, and Quang D Pham.  Importation and human-to-human transmission of a novel coronavirus in vietnam.  New England Journal of Medicine, 382(9):872–874, 2020.
  * [79] Sara Platto, Yanqing Wang, Jinfeng Zhou, and Ernesto Carafoli.  History of the covid-19 pandemic: origin, explosion, worldwide spreading.  Biochemical and biophysical research communications, 538:14–23, 2021.
  * [80] Mohammad Pourhomayoun and Mahdi Shakibi.  Predicting mortality risk in patients with covid-19 using machine learning to help medical decision-making.  Smart Health, 20:100178, 2021.
  * [81] Ravneet Punia, Lucky Kumar, Mohd Mujahid, and Rajesh Rohilla.  Computer vision and radiology for covid-19 detection.  In 2020 International Conference for Emerging Technology (INCET), pages 1–5. IEEE, 2020.
  * [82] Narinder Singh Punn, Sanjay Kumar Sonbhadra, and Sonali Agarwal.  Covid-19 epidemic analysis using machine learning and deep learning algorithms.  MedRxiv, 2020.
  * [83] M Qjidaa, A Ben-Fares, Y Mechbal, H Amakdouf, M Maaroufi, B Alami, and H Qjidaa.  Development of a clinical decision support system for the early detection of covid-19 using deep learning based on chest radiographic images.  In 2020 International Conference on Intelligent Systems and Computer Vision (ISCV), pages 1–6. IEEE, 2020.
  * [84] Mohammad Marufur Rahman, Md Motaleb Hossen Manik, Md Milon Islam, Saifuddin Mahmud, and Jong-Hoon Kim.  An automated system to limit covid-19 using facial mask detection in smart city network.  In 2020 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pages 1–5. IEEE, 2020.
  * [85] Pranav Rajpurkar, Jeremy Irvin, Kaylie Zhu, Brandon Yang, Hershel Mehta, Tony Duan, Daisy Ding, Aarti Bagul, Curtis Langlotz, Katie Shpanskaya, et al.  Chexnet: Radiologist-level pneumonia detection on chest x-rays with deep learning.  arXiv preprint arXiv:1711.05225, 2017.
  * [86] Jawad Rasheed, Alaa Ali Hameed, Chawki Djeddi, Akhtar Jamil, and Fadi Al-Turjman.  A machine learning-based framework for diagnosis of covid-19 from chest x-ray images.  Interdisciplinary Sciences: Computational Life Sciences, 13(1):103–117, 2021.
  * [87] Geetanjali Rathee, Sahil Garg, Georges Kaddoum, Yulei Wu, Dushantha Nalin K Jayakody, and Atif Alamri.  Ann assisted-iot enabled covid-19 patient monitoring.  IEEE Access, 9:42483–42492, 2021.
  * [88] Julien Riou and Christian L Althaus.  Pattern of early human-to-human transmission of wuhan 2019 novel coronavirus (2019-ncov), december 2019 to january 2020.  Eurosurveillance, 25(4):2000058, 2020.
  * [89] Tanzila Saba, Ibrahim Abunadi, Mirza Naveed Shahzad, and Amjad Rehman Khan.  Machine learning techniques to detect and forecast the daily total covid-19 infected and deaths cases under different lockdown types.  Microscopy Research and Technique, 2021.
  * [90] Carlos Sáez, Nekane Romero, J Alberto Conejero, and Juan M García-Gómez.  Potential limitations in covid-19 machine learning due to data source variability: A case study in the ncov2019 dataset.  Journal of the American Medical Informatics Association, 28(2):360–364, 2021.
  * [91] Pritam Saha, Debadyuti Mukherjee, Pawan Kumar Singh, Ali Ahmadian, Massimiliano Ferrara, and Ram Sarkar.  Graphcovidnet: A graph neural network based model for detecting covid-19 from ct scans and x-rays of chest.  Scientific Reports, 11(1):1–16, 2021.
  * [92] Ahmed T Sahlol, Dalia Yousri, Ahmed A Ewees, Mohammed AA Al-Qaness, Robertas Damasevicius, and Mohamed Abd Elaziz.  Covid-19 image classification using deep features and fractional-order marine predators algorithm.  Scientific Reports, 10(1):1–15, 2020.
  * [93] Sadman Sakib, Tahrat Tazrin, Mostafa M Fouda, Zubair Md Fadlullah, and Mohsen Guizani.  Dl-crc: Deep learning-based chest radiograph classification for covid-19 detection: A novel approach.  IEEE Access, 8:171575–171589, 2020.
  * [94] Fatima M Salman, Samy S Abu-Naser, Eman Alajrami, Bassem S Abu-Nasser, and Belal AM Alashqar.  Covid-19 detection using artificial intelligence.  ABC, 2020.
  * [95] Nitirajsingh Sandu and Shakir Karim.  The application of fast capsnet computer vision in detecting covid-19.  ABC, 2020.
  * [96] Rachna Sethi, Monica Mehrotra, and Dhaarna Sethi.  Deep learning based diagnosis recommendation for covid-19 using chest x-rays images.  In 2020 Second International Conference on Inventive Research in Computing Applications (ICIRCA), pages 1–4. IEEE, 2020.
  * [97] Prabira Kumar Sethy, Santi Kumari Behera, Pradyumna Kumar Ratha, and Preesat Biswas.  Detection of coronavirus disease (covid-19) based on deep features and support vector machine.  ABC, 2020.
  * [98] Ahmad Shalbaf, Majid Vafaeezadeh, et al.  Automated detection of covid-19 using ensemble of transfer learning with deep convolutional neural network based on ct scans.  International journal of computer assisted radiology and surgery, 16(1):115–123, 2021.
  * [99] Sourabh Shastri, Kuljeet Singh, Sachin Kumar, Paramjit Kour, and Vibhakar Mansotra.  Time series forecasting of covid-19 using deep learning models: India-usa comparative case study.  Chaos, Solitons & Fractals, 140:110227, 2020.
  * [100] Weiya Shi, Xueqing Peng, Tiefu Liu, Zenghui Cheng, Hongzhou Lu, Shuyi Yang, Jiulong Zhang, Mei Wang, Yaozong Gao, Yuxin Shi, et al.  A deep learning-based quantitative computed tomography model for predicting the severity of covid-19: a retrospective study of 196 patients.  Annals of Translational Medicine, 9(3), 2021.
  * [101] Junji Shiraishi, Shigehiko Katsuragawa, Junpei Ikezoe, Tsuneo Matsumoto, Takeshi Kobayashi, Ken-ichi Komatsu, Mitate Matsui, Hiroshi Fujita, Yoshie Kodera, and Kunio Doi.  Development of a digital image database for chest radiographs with and without a lung nodule: receiver operating characteristic analysis of radiologists’ detection of pulmonary nodules.  American Journal of Roentgenology, 174(1):71–74, 2000.
  * [102] Mohammad Shorfuzzaman and M Shamim Hossain.  Metacovid: A siamese neural network framework with contrastive loss for n-shot diagnosis of covid-19 patients.  Pattern recognition, 113:107700, 2021.
  * [103] Lucas Silva and Dalson Figueiredo Filho.  Using benford’s law to assess the quality of covid-19 register data in brazil.  Journal of public health, 43(1):107–110, 2021.
  * [104] Rajeev Kumar Singh, Rohan Pandey, and Rishie Nandhan Babu.  Covidscreen: Explainable deep learning framework for differential diagnosis of covid-19 using chest x-rays.  Neural Computing and Applications, pages 1–22, 2021.
  * [105] Ying Song, Shuangjia Zheng, Liang Li, Xiang Zhang, Xiaodong Zhang, Ziwang Huang, Jianwen Chen, Ruixuan Wang, Huiying Zhao, Yunfei Zha, et al.  Deep learning enables accurate diagnosis of novel coronavirus (covid-19) with ct images.  IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2021.
  * [106] A Spinelli and G Pellino.  Covid-19 pandemic: perspectives on an unfolding crisis.  Journal of British Surgery, 107(7):785–787, 2020.
  * [107] R Sujath, Jyotir Moy Chatterjee, and Aboul Ella Hassanien.  A machine learning forecasting model for covid-19 pandemic in india.  Stochastic Environmental Research and Risk Assessment, 34:959–972, 2020.
  * [108] Liang Sun, Zhanhao Mo, Fuhua Yan, Liming Xia, Fei Shan, Zhongxiang Ding, Bin Song, Wanchun Gao, Wei Shao, Feng Shi, et al.  Adaptive feature selection guided deep forest for covid-19 classification with chest ct.  IEEE Journal of Biomedical and Health Informatics, 24(10):2798–2805, 2020.
  * [109] Kenji Suzuki.  Overview of deep learning in medical imaging.  Radiological physics and technology, 10(3):257–273, 2017.
  * [110] Hui-Bin Tan, Fei Xiong, Yuan-Liang Jiang, Wen-Cai Huang, Ye Wang, Han-Han Li, Tao You, Ting-Ting Fu, Ran Lu, and Bi-Wen Peng.  The study of automatic machine learning base on radiomics of non-focus area in the first chest ct of different clinical types of covid-19 pneumonia.  Scientific reports, 10(1):1–10, 2020.
  * [111] Mohammadhossein Toutiaee, Xiaochuan Li, Yogesh Chaudhari, Shophine Sivaraja, Aishwarya Venkataraj, Indrajeet Javeri, Yuan Ke, Ismailcem Arpinar, Nicole Lazar, and John Miller.  Improving covid-19 forecasting using exogenous variables.  arXiv preprint arXiv:2107.10397, 2021.
  * [112] Jingxuan Tu, Marc Verhagen, Brent Cochran, and James Pustejovsky.  Exploration and discovery of the covid-19 literature through semantic visualization.  arXiv preprint arXiv:2007.01800, 2020.
  * [113] Muammer Turkoglu.  Covidetectionet: Covid-19 diagnosis system based on x-ray images using features selected from pre-learned deep features ensemble.  Applied Intelligence, 51(3):1213–1226, 2021.
  * [114] Jijo Pulickiyil Ulahannan, Nikhil Narayanan, Nishad Thalhath, Prem Prabhakaran, Sreekanth Chaliyeduth, Sooraj P Suresh, Musfir Mohammed, E Rajeevan, Sindhu Joseph, Akhil Balakrishnan, et al.  A citizen science initiative for open data and visualization of covid-19 outbreak in kerala, india.  Journal of the American Medical Informatics Association, 27(12):1913–1920, 2020.
  * [115] Elise van Nederveen Meerkerk.  Segmentation in the pre-industrial labour market: women’s work in the dutch textile industry, 1581–1810.  International review of social history, 51(2):189–216, 2006.
  * [116] Abdul Waheed, Muskan Goyal, Deepak Gupta, Ashish Khanna, Fadi Al-Turjman, and Plácido Rogerio Pinheiro.  Covidgan: data augmentation using auxiliary classifier gan for improved covid-19 detection.  Ieee Access, 8:91916–91923, 2020.
  * [117] Yuchai Wan, Hongen Zhou, and Xun Zhang.  An interpretation architecture for deep learning models with the application of covid-19 diagnosis.  Entropy, 23(2):204, 2021.
  * [118] Linda Wang, Zhong Qiu Lin, and Alexander Wong.  Covid-net: A tailored deep convolutional neural network design for detection of covid-19 cases from chest x-ray images.  Scientific Reports, 10(1):1–12, 2020.
  * [119] Lucy Lu Wang, Kyle Lo, Yoganand Chandrasekhar, Russell Reas, Jiangjiang Yang, Darrin Eide, Kathryn Funk, Rodney Kinney, Ziyang Liu, William Merrill, et al.  Cord-19: The covid-19 open research dataset.  ArXiv, 2020.
  * [120] Ningwei Wang, Hongzhe Liu, and Cheng Xu.  Deep learning for the detection of covid-19 using transfer learning and model integration.  In 2020 IEEE 10th International Conference on Electronics Information and Emergency Communication (ICEIEC), pages 281–284. IEEE, 2020\.
  * [121] Shuai Wang, Bo Kang, Jinlu Ma, Xianjun Zeng, Mingming Xiao, Jia Guo, Mengjiao Cai, Jingyi Yang, Yaodong Li, Xiangfei Meng, et al.  A deep learning algorithm using ct images to screen for corona virus disease (covid-19).  European Radiology, pages 1–9, 2021.
  * [122] Shuo Wang, Yunfei Zha, Weimin Li, Qingxia Wu, Xiaohu Li, Meng Niu, Meiyun Wang, Xiaoming Qiu, Hongjun Li, He Yu, et al.  A fully automatic deep learning system for covid-19 diagnostic and prognostic analysis.  European Respiratory Journal, 56(2), 2020.
  * [123] Xiaosong Wang, Yifan Peng, Le Lu, Zhiyong Lu, Mohammadhadi Bagheri, and Ronald M Summers.  Chestx-ray8: Hospital-scale chest x-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases.  In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 2097–2106, 2017.
  * [124] Xinggang Wang, Xianbo Deng, Qing Fu, Qiang Zhou, Jiapei Feng, Hui Ma, Wenyu Liu, and Chuansheng Zheng.  A weakly-supervised framework for covid-19 classification and lesion localization from chest ct.  IEEE transactions on medical imaging, 39(8):2615–2625, 2020.
  * [125] Salomón Wollenstein-Betech, Amanda AB Silva, Julia L Fleck, Christos G Cassandras, and Ioannis Ch Paschalidis.  Physiological and socioeconomic characteristics predict covid-19 mortality and resource utilization in brazil.  PloS one, 15(10):e0240346, 2020.
  * [126] Dongxia Wu, Liyao Gao, Xinyue Xiong, Matteo Chinazzi, Alessandro Vespignani, Yi-An Ma, and Rose Yu.  Deepgleam: A hybrid mechanistic and deep learning model for covid-19 forecasting, 2021.
  * [127] Bo Xu and Xiangfei Meng.  A deep learning algorithm using ct images to screen for corona virus disease (covid-19).  Preprint, 2021.
  * [128] Ramjeet Singh Yadav.  Data analysis of covid-2019 epidemic using machine learning methods: a case study of india.  International Journal of Information Technology, 12:1321–1330, 2020\.
  * [129] Yuki Yamada, Dominik-Borna Ćepulić, Tao Coll-Martín, Stéphane Debove, Guillaume Gautreau, Hyemin Han, Jesper Rasmussen, Thao P Tran, Giovanni A Travaglino, and Andreas Lieberoth.  Covidistress global survey dataset on psychological and behavioural consequences of the covid-19 outbreak.  Scientific data, 8(1):1–23, 2021.
  * [130] Xingyi Yang, Xuehai He, Jinyu Zhao, Yichen Zhang, Shanghang Zhang, and Pengtao Xie.  Covid-ct-dataset: a ct image dataset about covid-19.  arXiv preprint arXiv:2003.13865, 2020.
  * [131] Haochen Yao, Nan Zhang, Ruochi Zhang, Meiyu Duan, Tianqi Xie, Jiahui Pan, Ejun Peng, Juanjuan Huang, Yingli Zhang, Xiaoming Xu, et al.  Severity detection for the coronavirus disease 2019 (covid-19) patients using a machine learning model based on the blood and urine tests.  Frontiers in cell and developmental biology, 8:683, 2020.
  * [132] Seung Hoon Yoo, Hui Geng, Tin Lok Chiu, Siu Ki Yu, Dae Chul Cho, Jin Heo, Min Sung Choi, Il Hyun Choi, Cong Cung Van, Nguen Viet Nhung, et al.  Deep learning-based decision-tree classifier for covid-19 diagnosis from chest x-ray imaging.  Frontiers in medicine, 7:427, 2020.
  * [133] Tahmina Zebin and Shahadate Rezvy.  Covid-19 detection and disease progression visualization: Deep learning on chest x-rays for classification and coarse localization.  Applied Intelligence, 51(2):1010–1021, 2021.
  * [134] Wenhuan Zeng, Anupam Gautam, and Daniel H Huson.  On the application of advanced machine learning methods to analyze enhanced, multimodal data from persons infected with covid-19.  Computation, 9(1):4, 2021.
  * [135] Hai-tao Zhang, Jin-song Zhang, Hai-hua Zhang, Yan-dong Nan, Ying Zhao, En-qing Fu, Yong-hong Xie, Wei Liu, Wang-ping Li, Hong-jun Zhang, et al.  Automated detection and quantification of covid-19 pneumonia: Ct imaging analysis by a deep learning-based software.  European journal of nuclear medicine and molecular imaging, 47(11):2525–2532, 2020.
  * [136] Jianpeng Zhang, Yutong Xie, Guansong Pang, Zhibin Liao, Johan Verjans, Wenxing Li, Zongji Sun, Jian He, Yi Li, Chunhua Shen, et al.  Viral pneumonia screening on chest x-rays using confidence-aware anomaly detection.  IEEE transactions on medical imaging, 2020.
  * [137] Ruochi Zhang, Zhehao Guo, Yue Sun, Qi Lu, Zijian Xu, Zhaomin Yao, Meiyu Duan, Shuai Liu, Yanjiao Ren, Lan Huang, et al.  Covid19xraynet: A two-step transfer learning model for the covid-19 detecting problem based on a limited number of chest x-ray images.  Interdisciplinary Sciences: Computational Life Sciences, 12(4):555–565, 2020.
  * [138] Jie Zhao, Wei Cui, and Bao-ping Tian.  The potential intermediate hosts for sars-cov-2.  Frontiers in microbiology, 11, 2020.
  * [139] Tao Zhou, Huiling Lu, Zaoli Yang, Shi Qiu, Bingqiang Huo, and Yali Dong.  The ensemble deep learning model for novel covid-19 on ct images.  Applied Soft Computing, 98:106885, 2021.
  * [140] Jocelyn S Zhu, Peilin Ge, Chunguo Jiang, Yong Zhang, Xiaoran Li, Zirun Zhao, Liming Zhang, and Tim Q Duong.  Deep-learning artificial intelligence analysis of clinical variables predicts mortality in covid-19 patients.  Journal of the American College of Emergency Physicians Open, 1(6):1364–1373, 2020.
  * [141] Yazeed Zoabi, Shira Deri-Rozov, and Noam Shomron.  Machine learning-based prediction of covid-19 diagnosis based on symptoms.  npj digital medicine, 4(1):1–5, 2021.