∎

11institutetext: Corresponding author ∗ Walid Hariri 22institutetext: Labged
Laboratory, Badji Mokhtar Annaba University
22email<EMAIL_ADDRESS>33institutetext: Ali Narin 44institutetext:
Department of Electrical and Electronics Engineering, Zonguldak Bulent Ecevit
University
44email<EMAIL_ADDRESS>
# Deep Neural Networks for COVID-19 Detection and Diagnosis using Images and
Acoustic-based Techniques: A Recent Review

Walid Hariri  Ali Narin

(Received: date / Accepted: date)

###### Abstract

The new coronavirus disease (COVID-19) has been declared a pandemic since
March 2020 by the World Health Organization. It consists of an emerging viral
infection with respiratory tropism that could develop atypical pneumonia.
Experts emphasize the importance of early detection of those who have the
COVID-19 virus. In this way, patients will be isolated from other people and
the spread of the virus can be prevented. For this reason, it has become an
area of interest to develop early diagnosis and detection methods to ensure a
rapid treatment process and prevent the virus from spreading. Since the
standard testing system is time-consuming and not available for everyone,
alternative early-screening techniques have become an urgent need. In this
study, the approaches used in the detection of COVID-19 based on deep learning
(DL) algorithms, which have been popular in recent years, have been
comprehensively discussed. The advantages and disadvantages of different
approaches used in literature are examined in detail. The Computed Tomography
of the chest and X-ray images give a rich representation of the patient’s lung
that is less time-consuming and allows an efficient viral pneumonia detection
using the DL algorithms. The first step is the pre-processing of these images
to remove noise. Next, deep features are extracted using multiple types of
deep models (pre-trained models, generative models, generic neural networks,
etc.). Finally, the classification is performed using the obtained features to
decide whether the patient is infected by coronavirus or it is another lung
disease. In this study, we also give a brief review of the latest applications
of cough analysis to early screen the COVID-19, and human mobility estimation
to limit its spread.

###### Keywords:

Viral Pneumonia COVID-19 Deep learning Chest CT scan X-Ray Image Cough
analysis.

##  1 Introduction

The novel severe acute respiratory syndrome-related coronavirus (SARS-CoV-2)
started from Wuhan, China in December 2019 and spread to all the countries
worldwide. This virus caused pneumonia of unknown cytology and is named
COVID-19. This infectious disease has been classified as a public health
crisis of the international community concern on January 30, 2020, because of
its high infectivity and mortality. In 2021, many variants of COVID-19 have
been detected in different countries including UK, Brazil and India. The
spread of these variants and and the mortality rates are more important than
the previous ones. Figure 1 presents the daily cases of the Indian variant
reported by the COVID-19 study group in India. The lack of successful
diagnosis or preventive measures has led to a rise in the number of cases, an
increase in the cost of hospitalizations and palliative treatments. Therefore,
scientists and medical industries around the world incited to find a prompt
and accurate detection of COVID-19 for early prevention, screening,
forecasting, drug development, and contact tracing to save more time for the
scientific community and healthcare expert to pass to the next diagnosis stage
to reduce the death rate reverse transcription polymerase chain reaction (RT-
PCR) is recommended to diagnose COVID-19. Additionally, there are studies in
the literature using various imaging methods (computed tomography (CT) and
X-ray). xu2020deep ; gozes2020rapid ; ucar2020covidiagnosis ;
sethy2020detection ; zhang2020covid . It may occur in situations that
negatively affect these methods. The changes of viruses by the appearance of
new mutations make the classifications a more challenging task
grubaugh2020making . Moreover, one of the biggest problems with COVID-19
patients is viral pneumonia (VP). Differentiating between viral and non-viral
pneumonia (nVP) is not easy. Coexistence of COVID-19 and viral pneumonia can
have dire consequences.

Oxford COVID-19 Evidence Service Team Center follows some tips in identifying
these problems. Muscle pain, loss of sense of smell and shortness of breath
without pleuritic pain are the most common symptoms, especially in the case of
COVID-19 infection. On the other hand, symptoms such as bilateral positive
lung findings, tachycardia or tachypnea disproportionate to temperature, and
low temperature indicate VP (not COVID-19) symptoms Carl:2020 . nVP, however,
is most susceptible if it becomes rapidly unwell after a short period from the
appearance of symptoms and does not have similar symptoms of COVID-19,
pleuritic pain, or purulent sputum.

Figure 1: Cumulative daily cases in India StudyGroup2021 .

Many studies have been introduced to solve this problem, for instance, Zhang
et al. zhang2020viral  proposed to lessen the process of anomaly detection
into a one-class-classification problem using a confidence aware module. Deep
learning (DL) is then used for the classification task as shown in Figure 2.
Recent reviews show that the use of novel technology with artificial
intelligence (AI) and machine learning (ML) techniques considerably improves
the screening, contact tracing, forecasting, and drug and vaccine development
with high reliability.

Since the COVID-19 pandemic started, it has been clear that deep learning
algorithms from ML technologies seem to be used extensively to detect
COVID-19, VP, bacterial pneumonia (BP) and other similar cases. The advantages
and disadvantages of these studies should be evaluated. In this study, it is
aimed to present a detailed review on studies using DL approaches using
various images in the literature to detect COVID-19. In addition, studies that
detection of COVID-19 using acoustic sound data are included.

The rest of this paper is organized as follows: Section 2 presents the medical
imaging technologies. In Section 3 we review the most important DL methods
proposed to diagnose the COVID-19, as well as the recent advanced
applications. Section 4 presents some additional DL applications to fight
against COVID-19 such as acoustic analysis and human mobility estimation. In
Section 5 an overall discussion and proposed solutions have been presented to
accurately diagnose and to reduce the spread of the COVID-19. Conclusions,
future trends and challenges end the paper.

Figure 2: Distinguishing between VP cases (anomalies) and nVP cases and normal
controls zhang2020viral .

##  2 Medical imaging technologies versus RT-PCR test

The medical imaging field has considerably emerged in the last years offering
reliable automated methods for clinical decision making. It has received wide
acceptance by the scientists and the medical community. In the case of
COVID-19, CT scans and X-ray images can play a vital role in the early
diagnosis of the disease. Infected patients have clinical symptoms including
cough and fever, however, an important proportion of infected patients can be
asymptomatic. In Germany, it has been confirmed in the study of Rothe et al.
rothe2020transmission  that an asymptomatic patient was able to transmit the
virus to another patient. According to the study of Al-Tawfiq et al.
al2020asymptomatic  from 9 countries, 18 from 144 cases were asymptomatic, the
equivalent of 12.5%. The study has been done using the RT-PCR test.

Due to the high risk of transmission of COVID-19, accurate diagnostic methods
are urgently needed to prevent the spread of the virus and for humanity to
breathe comfortably. Besides being the gold standard of the RT-PCR test, the
results are time consuming (requires 5 to 6 hours) to obtain.In addition, the
high rate of false detection of RT-PCR test is questioned whether it is a good
diagnostic method. xie2020chest ; long2020diagnosis . In this case, it is
recommended that patients with typical imaging findings should be separated
ones from one another and more than one RT-PCR test should performed to avoid
misdiagnosis.

The X-ray, however, is an efficient screening method, it is fast at capturing,
cheaper than the RT-PCR test, and largely available worldwide. CT-scans, on
the other hand, can be obtained much faster and more accurately in the
presence of an efficient algorithm (notably DL algorithms) to accurately
identify the infected patients. In liu2019comparison , it has been proven that
DL offers highly promising results for medical diagnostics compared to health-
care professionals. Figure 3 presents the change that occurs in the COVID-19
pneumonia cases on some days. In the following section, detailed information
about CT and X-ray images is presented.

###  2.1 Chest Computed Tomography

CT is an imaging method that uses a special x-ray beam to create detailed
scans of areas inside the body (e.g. lungs, heart, blood vessels, airways, and
lymph nodes). These images are taken from different angles to generate
tomographic images which give the possibility to the radiographers to directly
see inside the body instead of surgery. CT images are considered to be an
effective way of making clinical decisions. They showed high efficiency in
diagnosing COVID-19 especially patients with false-negative RT-PCR results,
assuming a role for the CT as a reliable tool for COVID-19 diagnosis during
this epidemic period li2020coronavirus ; ai2020correlation ; xie2020chest ;
huang2020use . Therefore, the National Health Commission of the People’s
Republic of China suggested CT examination in monitoring disease progression
and controlling treatments of COVID-19 in its $6^{th}$ version of the
diagnosis and treatment program zhao2020interpretations .

Figure 3:  CT scans in the early fast gradually stage of COVID-19 pneumonia
cases. a: GGO plus reticular pattern on the forth day. b: GGO plus
consolidation on the third day. c: GGO on the second day. zhou2020imaging

###  2.2 X-Ray Image

Wilhelm Conrad Röntgen has discovered the first X-ray in 1895 during
experimenting with Lenard tubes and Crookes tubes. X-ray has a very important
role in the medical field, it can help in the prevention of infection,
diagnosis, and control. X-ray scans are used worldwide to diagnose the injured
part and for the detection or other diseases in order to treat patients
ghosh2018automatic . The X-ray facility is available even in the remotest
parts and thus X-ray images can be easily acquired for patients even in their
home or in their quarantine location. These images have been extensively used
for COVID-19 diagnosis narin2020automatic . The most common reported abnormal
in Chest X-ray (CXR) findings are ground-glass opacities (GGOs) yoon2020chest
. Figure 4 presents an example of an X-ray scan for COVID-19 patients. CXR is
the most widely used imaging technology by researchers because it is easily
available and inexpensive. However, GGOs are often the first sign of a
diagnosis of COVID-19 pneumonia.

Figure 4: An example of X-ray image for a COVID-19 patient. Figure 5: Taxonomy
of deep learning based-approaches for COVID-19 diagnosis.

##  3 Deep learning approaches in the COVID-19 pandemic

DL is a subset of ML that offers considerable power for improving the accuracy
and speed of diagnosis by automating the screening through medical imaging in
collaboration with radiologists and/or physicians. Subsequently, it has
received wide acceptance and interest by the medical community leads to
emphasizing the development of such diagnostic technologies liu2019comparison
. In the following, we review the most important DL approaches adopted to
diagnose the pneumonia of COVID-19 since its spread in December 2019 until
today. Figure 5 presents the taxonomy of these approaches using different
images and acoustic features. In the following, we detail each of the
distinguished nine groups:

###  3.1 Generic deep learning

Generic DL methods without any specific modification have been proposed to
detect COVID-19. For example, Wang el al. wang2020fully  have used CT images
of 5,372 patients from 7 different cities in China to train a deep neural
network (DNN).

Pneumonia Detection Challenge dataset (RSNA) is used in luz2020towards  to
train a DL model in order to locate lung opacities on chest radiographs. RSNA
dataset contains two classes: Normal and Pneumonia (non-normal). The total of
16,680 images have been used from this data set where 8,066 are from healthy
class (normal), whereas 8,614 as classified as pneumonia.

The authors in song2020deep  collected from two hospitals of in China the CT
images of 88 infected patients (COVID-19), 101 patients diagnosed with
bacteria pneumonia, where the rest are healthy (86 persons). Using this
dataset, they applied a DL-based CT diagnosis system namely: DeepPneumonia to
localize the principal lesion features, especially GGO and thus to identify
the infected patients. The first step is the segmentation of the lung region.
Next, they introduced the DRE-Net (Details Relation Extraction neural network)
to draw the top-K features in the CT images and to receive the image-level
predictions. Finally, the image-level predictions is used to diagnose the
patient.

Another generic DL framework is proposed in zhang2020automated  to
automatically extract and analyze regions with high possibility to be infected
with COVID-19. To do so, the authors applied a segmentation stage using a DL-
based technique. Then, the infected regions were processed and quantized using
specific metrics in the CT scan.

We can also find generic convolutional neural networks in which the authors
use the generic CNN trained with their datasets without any combination with
other ML algorithms or pre-trained models. For example, in fu2020deep , the
authors trained the CNN model with the data collected from Wuhan Jin Yin-Tan
hospital in order to classify the CT images into one of the five following
classes: healthy lung, COVID-19, pneumonia, non-COVID-19 VP, BP and pulmonary
tuberculosis.

###  3.2 Transfer learning

Transfer learning (TL) is a ML technique in which a trained model for one task
is redesigned in a related second task (see Figure 6). This approach is
explicitly useful when there are not sufficient datasets like in the case of
COVID-19 in order to either reduce the necessary fine-tuning data size or
improve performance. TL can be used in two scenarios: supervised (with labeled
data from the target domain) or unsupervised (without any labeled data from
the target domain: the pretraining process is supervised, but unsupervised
during fine-tuning). A DNN is proposed in jaiswal2020covidpen  to detect
COVID-19 using X-ray images. To do so, the authors applied a TL approach on
the deep Pruned EfficientNet model. Then, it has been interpolated by post-hoc
analysis to be able to explain the obtained predictions. TL based-framework
for the detection of pneumonia is proposed in chouhan2020novel . The features
have been extracted from X-ray images using five different pre-trained models:
DenseNet121, ResNet18, GoogLeNet, AlexNet and InceptionV3. Next, an ensemble
model has been added to combine outputs from all pre-trained models. The
obtained results are as follows: accuracy of 96.4%; recall of 99.62% on non-
trained data from the Guangzhou Women and Children’s Medical Center database.

Fine-tuned deep TL with generative adversarial network (GAN) is presented in
khalifa2020detection  to learn a limited dataset and to avoid the overfitting
problem. To do so, the authors applied the pre-trained models: Squeeznet,
AlexNet, GoogLeNet, and Resnet18 as deep TL models to detect pneumonia from
chest x-rays. Applying a combination of GAN and deep transfer models enhanced
the accuracy of the proposed system and realized 99%. After applying image
preprocessing algorithms to the chest X-ray images to identify and remove
diaphragm regions, the pre-trained VGG-16 model simonyan2014very  has been
fine-tuned in heidari2020improving  using the obtained images to predict
COVID-19 infected pneumonia. Another work proposed in apostolopoulos2020covid
to detect the COVID-19 in small medical image datasets. To do so, they worked
with two different data sets from public databases. In the first dataset,
there are 224 COVID-19, 700 BP and 504 Normal X-ray images. The second dataset
includes 224 COVID-19, 714 BP and VP, and 504 Normal X-ray images. They
obtained 96.78% accuracy, 98.66% sensitivity and 96.46% specificity
performance values.

Multi-Channel TL-based method with X-ray images have been proposed in
misra2020multi . Multi-channel pre-trained ResNet model is then used to
perform the diagnosis of COVID-19. To classify the X-ray images on a one-
against-all strategy, three ResNet models have been retrained. The three
allowed classifications are: 1) normal or diseased, 2) pneumonia or non-
pneumonia, and 3) COVID-19 or non-COVID19 individuals. The method achieved a
precision of 94% and a recall of 100%. Other TL-based methods can be found in
minaee2020deep ; maghdid2020diagnosing ; benbrahim2020deep ;
haghanifar2020covid ; abbas2020classification ; rahaman2020identification ;
perumal2020detection ; loey2021hybrid ; pham2021classification .

Figure 6: An example of transfer learning process for COVID-19 detection.

###  3.3 Augmentation and Generation Techniques

Recently, generative adversarial networks (GANs) are considered the most
powerful and successful method for data augmentation. Since the outbreak of
COVID-19 is recent, it is difficult to gather a significant amount of
radiographic images and datasets in such a short time. Therefore, DL networks
especially (CNNs) need additional training data to overcome this problem and
to enhance the efficiency of CNN in detecting COVID-19 (See Figure 7). Various
methods have been applied the GANs for this reason. For instance, in
waheed2020covidgan , authors generate more X-ray images using Auxiliary
Classifier Generative Adversarial Network (ACGAN) based on the CovidGAN model.
Accordingly, the classification accuracy has been significantly enhanced from
85% using the CNN alone, to 95% using the ACGAN with CovidGAN model.

Also, to handle the problem of the lack of datasets for COVID-19, Loey et al.
loey2020deep  proposed classical data augmentation techniques along with
Conditional GAN (CGAN) on the basis of a deep transfer learning model for
COVID-19 detection using CT images. Similar representation has been used in
loey2020within  to classify the CT images into the following four classes :
the COVID-19, normal, pneumonia bacterial, and pneumonia virus. To do so, the
authors have used a dataset of 307 images. Three deep transfer models are then
carried out in this work for investigation. The models are the Googlenet,
Alexnet and Restnet18. three strategies have been conducted, in each strategy
the authors applied a different deep TL using the three pre-trained models
mentioned above. The testing accuracies achieved by the Googlenet, Alexnet and
Restnet18 are 80.6%, 85.2% and 100%, respectively.

Another method proposed in karakanis2020lightweight  aims to generate
synthetic medical images using DL CGAN to overcome the dataset limitation that
leads to over-fitting. The proposed model has been implemented in a form to
support a lightweight architecture without transfer learning without
performance degradation. It can deal with any non-uniformity in the data
distribution and the limited accessibility of training images in the classes.
It consists of a single convolutional layer with filter size 32 and kernel 4
$\times$ 4, followed by ReLU activation function and Max Pooling layer for
down-sampling the image (input representation) and enabling feature
extraction. After a flatten layer there exists a dense layer of size 128,
followed by dropout and a final dense layer with softmax activation function
for a binary output. Other methods based on data augmentation to detect the
COVID-19 can be found in ahmed2021convid .

Figure 7: Generative adversarial network representation for COVID-19
detection.

###  3.4 Autoencoder-based models

Another ML technique to handle the problem of insufficient data for the
affected COVID-19 cases is the Autoencoder (AE) baldi2012autoencoders . It is
a neural network method with competent data encoding and decoding strategies
used for unsupervised feature learning. The AE models are comprised of two
main steps: encoder and decoder. The input samples are mapped typically to a
lower-dimensional space with beneficial feature representation in the encoding
step. In the second step, the decoding consists of reverting data to its
original space, trying to create data from lower space representation. Figure
8 shows the conceptual diagram of AE with its two main steps. The advantage of
adopting such unsupervised classification to handle the problem of COVID-19
detection compared to its counterpart (supervised classification) is to avoid
the long time spent in assembling large amounts of data which could increase
the risk of mortality and postpones medical care. For example, in
khoshbakhtian2020covidomaly , the authors introduced the COVIDomaly which aims
to diagnose new COVID-19 cases using a convolutional autoencoder framework.
They tested two strategies on the COVIDX dataset acquired from the chest
radiographs by training the model on chest X-rays: the first strategy used
only healthy adults, the second tested healthy and BP, and infected adults
with COVID-19. Using 3-fold cross-validation, they obtained a pooled Receiver
Characteristic Operator-Area Under the Curve (ROC-AUC) of 76.52% and 69.02%
with the two strategies respectively.

In goel2020efficient , the authors extracted discriminative features from the
autoencoder and Gray Level Co-occurence Matrix using CT images. The obtained
features are then combined with random forest classifier for COVID-19
detection. They achieved the following results: accuracy of 97.78%,
specificity of 98.77% and recall of 96.78%. Other autoencoder-based methods
can be found in berenguer2020explainable ; shoeibi2020automated ;
khobahi2020coronet .

Figure 8: An example of Autoencoder model for COVID-19 detection.

###  3.5 Pre-trained Deep Neural Networks

Pre-trained models were originally trained on existing large-scale labeled
dataset (e.g. ImageNet) and later fine-tuned over the chest CT and X-ray
images to accomplish the diagnosis process. The last layer in these models has
been removed and a new fully connected (FC) layer is added with an output size
of two that represents two separate classes (COVID-19 or normal). In the
obtained models, only the final FC layer is trained, while other layers are
initialized with pre-trained weights nayak2020application . These models can
be a very useful solution to the lack of large datasets for COVID-19. However,
some challenges exist. One of the risen problems here is that the transfer
across datasets from a domain to another can lead to deterioration of
performance due to the gap existing between the domains. This is often the
case with medical images taken from different centers. Moreover, there is an
over-fitting problem with small amounts of COVID-19 datasets. Therefore, pre-
trained models are generally used with some particular modifications in order
to avoid the over-fitting problem.

In nayak2020application , eight pre-trained CNN models have been compared
including GoogleNet, AlexNet, MobileNet-V2, VGG-16, SqueezeNet, ResNet-50;
ResNet-34 and Inception-V3. The obtained results for the classification of
COVID-19 from normal cases show that ResNet-34 outperformed the other pre-
trained models and achieved an accuracy of 98.33%. This evaluation has been
conducted on a total of 286 scans of COVID-19 and normal classes as a training
set, and 120 scans for the test (60 scans for each class). When dealing with
the augmented dataset, the total of the training scans is 1002, where 428
scans are used for the validation and 120 scans for the test. ResNet-18 has
been applied in oh2020deep  using limited training datasets and achieved a
sensitivity of 100% and 76.90% of precision for the COVID-19 class. Figures 9
and 10 show an example of AlexNet and VGG-16 architectures respectively.
DenseNet201 pre-trained model is used in jaiswal2020classification  on chest
CT images. To classify the patients into positive or negative COVID-19, deep
transfer learning is carried out, and obtained a training accuracy of 99.82%,
and validation accuracy of 97.48%. The extreme version of the Inception
(Xception) model is applied in das2020automated  and achieved an accuracy of
97.40%, f measure of 96.96%, sensitivity of 97.09% and specificity of 97.29%
for three classes COVID-19, pneumonia, and other diseases. Hemdan et al.
hemdan2020covidx  proposed COVIDX-Net framework to diagnose the COVID-19 cases
using X-ray images. It includes three main steps to accomplish the diagnostic
process of the COVID-19 as follows: pre-processing, Training Model and
Validation, Classification. In consequence of the absence of public COVID-19
datasets, the experiments are carried out on 50 Chest X-ray images where only
25 have been diagnosed with COVID-19, for the validation. The COVIDX-Net
combined the following seven different architectures of deep CNN models:
VGG-19, DenseNet121, InceptionV3, ResNetV2, Inception-ResNet-V2, Xception, and
the second version of Google MobileNet. Figure 11 presents an overview of the
COVIDX-Net framework. Another model called InstaCovNet-19 makes use of five
pre-trained models including Xception, ResNet101, InceptionV3, MobileNet, and
NASN is proposed in gupta2020instacovnet . Two classification strategies have
been conducted : (COVID-19, Pneumonia, Normal) and (COVID, NON-COVID). Very
high precision and classification accuracy have been achieved using the two
strategies (See Table 1). Similar method has been proposed by Narin et al.
narin2020automatic  using five pre-trained CNNs and three three different
binary datasets including COVID-19, normal (healthy), bacterial and viral
pneumonia patients. Gour et al. gour2020stacked  proposed a new CNN model
based on the VGG-19. They used a 30-layered CNN model for the training with
X-ray images, and obtained sub-models using logistic regression. Other methods
using pre-trained CNNs can be found in makris2020covid ; afshar2020covid ;
kumar2020blockchain .

Figure 9: AlexNet architecture proposed in krizhevsky2017imagenet . Figure 10:
VGG-16 architecture proposed in simonyan2014very . Figure 11: COVIDX-Net
framework hemdan2020covidx .

###  3.6 3D Convolutional Neural Networks

3D CNN models have also been used in the literature. They mainly extract 3D
features from the segmented 3D lung region using CT images. For example, Wang
et al. wang2020weakly  segmented the lung region a pre-trained UNet model. The
obtained volumes were fed into the proposed DeCoVNet (3D deep convolutional
neural Network to Detect COVID-19). A weakly-supervised classification is then
applied and achieved high COVID-19 classification performance and good lesion
localization results. Muller et al. muller2020automated  have also used UNet
model instead of computational complex CNNs to reduce the over-fitting problem
during the segmentation of the infected lung region. In wang2020prior ,
3D-ResNet is applied for end-to-end training to classify the acquired lung
images into pneumonia or healthy.

In order to predict the risk of COVID-19, Yang et al. yang2020end  applied
end-to-end training from CT images using the 3D Inception V1 model pre-trained
on the ImageNet dataset. The obtained accuracy was 95.78% overall, and 99.4%
on a a part of the dataset containing 1,684 COVID-19 patients. Li et al.
li2020artificial  introduced a 3D DL system that aims to early detect the
COVID-19, called COVNet. The COVNet model is composed essentially of
RestNet50, which have a range of CT scans as entry and produces features for
the equivalent scans. The obtained features from all scans are then involved
by a max-pooling process. The final feature map is used as an input to a fully
connected layer and softmax activation function to produce an output of a
likelihood result for the three classes: COVID-19, non-pneumonia and
Community-acquired pneumonia (CAP). Han et al. han2020accurate  introduced a
deep 3D multiple instance learning to detect the COVID-19 using CT images.
High accuracy has been achieved (97.9%) and AUC of 99.0%. Other 3D CNN-based
methods can be found in de2020improving ; liu20203d .

###  3.7 Combination of Generic CNNs with traditional ML algorithms

Another strategy is to use CNN models differently by combining them with
traditional ML algorithms.

In stephen2019efficient , the authors presented a CNN model trained on X-ray
images to recognize pneumonia. The proposed architecture consists of a
combination of the convolution, max-pooling, and rating layers. The obtained
features comprise four convolutional layers, a max-pooling layer, and a RELU
activator between them. The traditional ML algorithm ANN (Artificial neural
network) is finally applied for classification. ANN and AlexNet architecture
have been combined in aslan2020cnn  to systematically find out COVID-19
pneumonia subjects using CT scans. Firstly, a segmentation using ANN algorithm
is performed to localize the lungs. Next, COVID-19 classes are augmented to
produce more images. Finally, pre-trained Alexnet architecture is used in one
time with only a transfer learning process, the obtained accuracy is 98.14%.
And with additional layer namely called (Bidirectional Long Short-Term
Memories) in the second time, with an accuracy 98.70%. Nour et al.
nour2020novel  proposed a scratch CNN model including five convolution layered
serial network. Three ML algorithms have been trained on the obtained deep
features involving k-NN, SVM, and DT. The highest accuracy is obtained by the
SVM with 98.97%.

Instead of using pre-trained deep CNNs only as feature extractor, in
ismael2020deep , two other strategies have been conducted to accurately
classify Chest X-ray images into positive of negative COVID-19 including fine-
tuning strategy and end to end training. The following models have been used
as a feature extractor : ResNet101, VGG19, ResNet50, ResNet18, and VGG16 where
SVM is used for ML-based classification. Whereas, a new CNN model is used for
the fine-tuning strategy. Finally, end-to-end training with a dataset of 180
COVID-19 and 200 normal is carried out as a third strategy. 94.7% of accuracy
is achieved using ResNet50 model and SVM classifier, where fine-tuned strategy
with ResNet50 model achieved 92.6%. Finally, the end-to-end training strategy
of the developed CNN model realized a 91.6% result. Deep CNN and long short-
term memory (LSTM) have been combined in islam2020combined  to diagnose
COVID-19 automatically from X-ray images. The obtained accuracy of the
classification of three classes (COVID-19, normal, and other pneumonia) is
99.4%. Similar methods that combine deep features and classical ML techniques
can be found in sethy2020detection .

###  3.8 Ensemble models

Handling the problem of COVID-19 detection using a single DL model without any
specific addition might not achieve a high accuracy classification using CXR
images or CT scans. For this reason, the use of many DL models combined with
each other can be a good solution, namely: ensemble model and the learning
approach is called ensemble learning. For example, the authors in
sitaula2020attention2  proposed a DL model namely Attention-based VGG-16. This
model used VGG-16 to capture the spatial relationship between the ROIs in CXR
images. By using an appropriate convolution layer (4th pooling layer) of the
VGG-16 model in addition to the attention module, they added a novel DL model
to perform fine-tuning in the classification process. In hall2020finding
ensemble of three pre-trained models including Resnet50 and VGG16 and an own
small CNN is applied for a test set of 33 new COVID-19 and 218 pneumonia
cases. The overall accuracy realized is of 91.24%. Shalaf et al.
shalbaf2020automated  an ensemble deep transfer learning system with 15 pre-
trained CNN architectures on CT images. They obtained the following results:
accuracy (85%), precision (85.7%) and recall (85.2%).

###  3.9 Smart phone applications

To further automate the screening of COVID-19 and to make it faster, mobile
phones can be a very interesting framework for that due to their facility and
numerous sensors with important computing proficiencies. Specifically, a
smartphone has is able to scan CT images of COVID-19 patients to use them for
analysis screening. Moreover, multiple CT images of the same COVID-19 patient
can be gathered into one smartphone for similarity examination of how
disfigurement have been developed purswani2019big . However, the computing
capability of a mobile to treat a large amount of data is lower than a grand
machine or a computer. Therefore, a lightweight representation is needed to
accomplish this task. Consequently, various recent methods have been proposed
to detect COVID-19 in mobile devices using a slight representation. In
zulkifley2020covid , a lightweight DL model namely LightCovidNet has been
offered to detect COVID-19 using a mobile platform. To enhance the performance
of the proposed model, supplementary data have been generated and added to the
training dataset using the conditional deep convolutional GAN. In order to
reduce the memory usage of the proposed model, five units of feed-forward CNN
are built using separable convolution operators. Multi-scale features are then
learned to be suitable for the X-ray images which have been acquired from all
over the world separately. Instead of COVID-19 diagnosis and detection,
various lightweight applications have been introduced to delay the spread of
the virus. These applications could be designed to be compatible with the
capabilities of a smartphone to further speed up their operation. Among these
applications we can find: masked face recognition hariri2020efficient , facial
mask detection chen2020face ; chua2020face , social distance monitoring
ahmed2020deep ; rezaei2020deepsocial  and human mobility estimation
xiong2020mobile . Other mobile-based technique using to fight against COVID-19
has been proposed in maghdid2020novel . Using DL algorithms, the authors
arrived to efficiently evaluate the level of pneumonia and thus to determine
whether it is a COVID-19 case or not.

##  4 Deep learning for other applications

Other applications have been proposed to fight against COVID-19 induced
pneumonia, the most important are cough detection and human mobility
estimation.

###  4.1 Cough Detection

In addition to the DL approaches using X-ray and chest CT scans for COVID-19
detection, scientists affirm that audio sounds generated by the respiratory
system can be diagnosed and analyzed to decide whether the patient is infected
or not. Therefore, cough analysis has been used to screen and diagnose
COVID-19. ML techniques can supply useful cues enabling the development of a
diagnostic instrument. To do so, cough data of COVID and non-COVID is
required. Accordingly, Sharma et al. sharma2020coswara  proposed a database
called Coswara, of respiratory sounds, namely, cough, breath, and voice. Some
experiments have been recently carried out to screen COVID-19 from acoustic
features, for example, in hassan2020covid , Recurrent Neural Network (RNN) has
been used in its new architecture, namely the Long Short-Term Memory (LSTM) to
extract six speech features from a collected dataset (i.e. Spectral centroid,
Spectral Roll-off, Zero-Crossing Rate, Zero-Crossing Rate, MFCC, and MFCC). In
this work, 70% of the data was used for training, and 30% for testing. The
obtained results show that the best accuracy is achieved for breathing sound,
reaching up to 98.2% followed by cough sounds, an accuracy of 97% is attained.
Whereas, the accuracy of voice analysis is of 88.2%.

A smartphone application using cough-based diagnosis for COVID-19 detection is
proposed in imran2020ai4covid . This application is based on an AI-powered
screening solution called AI4COVID-19. Its principle is to send three 3 second
cough sounds to an AI engine running in the cloud, and give a result during
two minutes. To overcome the lack of COVID-19 cough training data, the authors
applied transfer learning using ESC-50 dataset piczak2015esc  that contains 50
classes of cough and non-cough sounds acquired using a smartphone. Figure12
presents the offered system architecture and a drawing of AI4COVID-19. The
obtained results show high overall accuracy of 95.60%, a sensitivity of
96.01%, a specificity of 95.19%, and precision of 95.22%. Schuller et al.
schuller2020covid  studied what computer audition could possibly contribute to
the ongoing battle against the COVID-19. Other recent acoustic analysis for
the detection of COVID-19 can be found in deshpande2020audio ;
alsabek2020studying ; pal2020pay ; quatieri2020framework ; laguarta2020covid ;
pal2020pay ; deshmukh2020interpreting .

Figure 12: AI4COVID-19 system architecture imran2020ai4covid .

###  4.2 Estimating Human Mobility

Human mobility (movement) is one of the main factors that promote the
transmission of the virus. Policy-makers find huge difficulties to find an
optimal protocol to insure the social distancing and barrier measures. To
solve this problem, Bao et al. bao2020covid  proposed a system that aims to
evaluate and estimate maps of people movement responses by learning from
existing ground truth data. The proposed system is based on a DL based-data
generation called COVID-GAN. It merges a diversity of features involving
contextual features, COVID-19 details and data history, as well as policies
from various origins such as news, reports and SafeGraph. Experiment results
showed that COVID-GAN can well imitate real-world human movement reactions and
the area-constraint-based correction can considerably upgrade the solution
value. To further explain the relation between people mobility and COVID-19
contamination, Xiong et al. xiong2020mobile  presented a study using mobile
device data to give more insights to decision makers about the national
mobility tendencies before and during the pandemic.

##  5 Discussion

Table 1 includes some studies conducted with DL models. It can be seen in
Table 1 that studies have focused on two popular images, namely CT and X-ray
images. The most used of these is X-ray images. This is because it is easily
available everywhere. In addition, both its low memory space and high results
in its performance encouraged researchers to use X-ray images. In addition,
the greater availability of X-ray data from COVID-19 patients in public
databases has led to the large number of these studies. In articles in the
field of medicine, it is often stated that CT images show higher performance.
However, these high accuracies are not seen in DL based CAD systems. This may
be because radiologists can easily distinguish patients from CT images. CT
images have too many cross-sections of the same person. They are much more
complex than X-ray images. This complex situation is a disadvantage in
distinguishing them in DL methods. The combination of X-ray and CT images,
performed in few studies, also shows that it gives good results. It can be
said that studies are carried out with multi-class solutions rather than the
binary classification problem. It is seen that studies carried out with 3D
data have lower performance. It has mostly been studied with 2D data. The
results obtained from these data are very high. Considering the number of
data, it is known that DL models work stably with a lot of data. According to
this fact, the number of data used is not sufficient. It can be stated that
this is one of the biggest problems in the detection of COVID-19. It is very
important to compare the studies conducted with the data obtained from many
different centers. Otherwise, the accuracy of the studies can be fooled. For
this, we recommend that data collected from many different centers be offered
to researchers from a single center. However, in studies conducted with a
small number of data, it is seen that pre-trained models are used to ensure
high model training. Especially Keras, Tensorflow, PyTorch and MATLAB also
include these pre-trained models. The biggest problem here is that these
models are trained with the ImageNet dataset. Having a lot of data belonging
to very different classes in the ImageNet dataset can reduce the trust in
these models. However, it is seen that the performances obtained are also very
high.

Traditional ML algorithms are also used by using the feature extraction part
of DL models. This approach appears to improve performance. It is generally
seen that the SVM algorithm is used. Most of the studies do not use any cross-
validation (CV) method. We think that this decreases the reliability of the
results. Because it is not known how the test data are distinguished. A very
high performance test dataset can be created. This overestimates the results
obtained. It can be noted that a CV method should be used in which the whole
data set can be tested in studies. Although there are many studies using DL-
based methods, it is very difficult to produce sufficiently transparent,
stable and reliable models. It was clearly stated above that there are many
parameters affecting the results.

As an alternative to the studies performed on X-ray and CT images, researches
on the detection of COVID-19 are carried out with sound and cough-based
acoustic sound analysis. Alternative approaches are very important in the
detection of COVID-19. It may be possible to save more lives with real-time
detection and diagnosis systems with online scanning systems installed with
mobile or computer. As a result of all these approaches, namely RT-PCR test,
DL-based X-ray and CT images, detection and acoustic examinations, it is
predicted that patients with COVID-19 can be detected more stable and with
higher accuracy. Because, considering that the epidemic started with a person,
it is very important to correctly diagnose even a person.

Table 1: Summary of DL-based methods for COVID-19 pneumonia classification.
C.V: refers to Cross validation. Tr: refers to training, Val: refers to the validation, AE refers to Autoencoder. Sens refers to the Sensitivity. Spec refers to the Specificity. Acc refers to the Accuracy. Author | Modality | Dataset | 2D/3D | All data | All COVID-19 | Network and technique | C.V | Sens(%) | Spec(%) | Acc (%)
---|---|---|---|---|---|---|---|---|---|---
narin2020automatic  | CXR |  COVID-19/normal COVID-19/pneumonia COVID-19/bacterial | 2D |  3141 1834 3113 | 341 | 5 pre-trained CNNs | 5 | / | / |  96.1 99.5 99.7
wang2020fully  | CT | COVID-19/pneumonia/normal | 3D | 1,266 | 924 | DNN | / |  Tr: 78.93 Val: 80.39 |  Tr: 89.93 Val: 81.16 | /
song2020deep  | CT | COVID-19/normal/bacterial | 2D | 275 | 88 | DRE-Net | / | 93 | 96 | 99
khalifa2020detection  | CXR | pneumonia dataset | 2D | 624 | 50 | GAN + TL | / | / | / | 99
apostolopoulos2020covid  | CXR | COVID-19/normal/bacterial | 2D | 1,427 | 224 | MobileNet v2 | 10 | 98.66 | 96.46 | 94.72
misra2020multi  | CXR | COVID-19/pneumonia/normal | 2D | 6,008 | 184 | Three ResNet models | 5 | / | / | 93.9
khoshbakhtian2020covidomaly  | CXR | COVID-19/pneumonia/normal | 2D | 8,850 | 498 | AE : COVIDomaly | 3 | / | / | 76.52
nour2020novel  | CXR | COVID-19/pneumonia/normal | 2D | 2,905 | 219 | CNN+k-NN+SVM | / | / | / | 98.70
aslan2020cnn  | CXR | COVID-19/pneumonia/normal | 2D | 2,905 | 219 | ANN+AlexNet | / | / | / | 98.97
oh2020deep  | CXR | COVID-19/pneumonia/normal | 2D | 502 | 180 | ResNet-18 | / | 76.90 | 100 | /
hall2020finding  | CXR | COVID-19/pneumonia/normal | 2D | 2,905 | 219 |  Ensemble:Resnet50 and VGG16 | 10 | / | / | 91.24
jaiswal2020classification  | CXR | COVID-19/normal | 2D | 2,492 | 1,262 | TL and DenseNet201 | / | / | / | 99.82
das2020automated  | CXR | COVID-19/pneumonia/normal | 2D | / | / | Xception | / | 97.09 | 97.29 | 97.40
ismael2020deep  |  CXR | COVID-19/normal | 2D | 380 | 180 |  5 pre-trained models \+ SVM | / | / | / | 94.7
gupta2020instacovnet  |  CXR |  COVID-19/pneumonia/normal COVID/non-COVID | 2D | 2905 | 219 | pre-trained models | / | / | / |  99.08 99.53
makris2020covid  | CXR | COVID-19/pneumonia/normal | 2D | / | / | 5 pre-trained CNNs | / | / | / | 95
afshar2020covid  | CXR |  Bacterial, Non-COVID Viral, COVID-19 | 2D | / | / | 5 COVID-CAPS | / | 90 | 95.8 | 95.7
minaee2020deep  | CXR | COVID-19/normal | 2D | 5,000 | 184 | 5 TL+pre-trained models | / | 100 | 98.3 | /
maghdid2020diagnosing  | CXR+CT | COVID-19/normal | 2D | 526 | 238 | TL+AlexNet model | / | 72 | 100 | 94.1
benbrahim2020deep  | CXR+CT | COVID-19/normal | 2D | 320 | 160 |  TL+InceptionV3 and ResNet50 | / | 72 | 100 | 99.01
islam2020combined  | CXR | COVID-19/pneumonia/normal | 2D | 4,575 | 1,525 | LSTM+CNN | / | 99.2 | 99.9 | 99.4
yang2020end  | CXR | COVID-19/pneumonia/normal | 2D |  4,448 101 |  2,479 52 | 3D Inception V1 | 10 |  / 98.08 |  / 91.30 |  95.78 93.3
zulkifley2020covid  | CXR | COVID-19/pneumonia/normal | 2D | 1343 | 446 |  Conditional GAN : LightCovidNet | 5 | / | / | 97.28
heidari2020improving  | CXR | COVID-19/normal | 2D |  Total: 8,504 Training: 6,899 |  Total: 445 Training: 366 | TL VGG-16 model | / | 98.0 | 100 | 94.5
shalbaf2020automated  | CT | COVID-19/normal | 2D | 746 | 349 |  TL+ Ensemble of 15 pre-trained models | / | / | / | 85
goel2020efficient  | CT | COVID-19/normal | 2D | 2,482 | 1,252 | AE+random forest | / | / | 98.77 | 97.87
hemdan2020covidx  | CXR | COVID-19/normal | 3D | 50 | 25 | COVIDX-Net | / | 100 | 80 | /

##  6 Conclusion

Although the RT-PCR test is considered the gold standard for COVID-19
diagnosis, it is time-consuming to make a decision because of high false-
negative levels in the results. Therefore, medical imaging modalities such as
chest X-ray and chest CT scans are the best alternative according to
scientists. Chest X-ray radiography is of low cost and low radiation dose, it
is available and easy to use in general or community hospitals. This review
presents a detailed study of the existing solutions that are mainly based on
DL techniques to early diagnose the COVID-19. This study gives more of an
insight into the scientists’ and decision-makers’ thought processes - not only
during the wave periods but also during that of the vaccination that could
require real-time mass testing. The lack of data, however, is the mandatory
problem to achieve efficient and real-time results. Many solutions have been
presented and discussed in this review study to give more ideas to future
trends and also for eventual future diseases that might suffer from the
missing-data problem. We believe that with more public databases, better DL
based-approaches can be developed to detect and diagnose the COVID19
accurately. Also, when policy-makers and citizens are making their best to
submit to the difficult constraints of lockdown and social distancing, AI can
be used to create more intelligent robots and autonomous machines to help
health workforce and to reduce their workload by disinfection, working in
hospitals, food distributing and helping the patients. The challenge of this
solution is that people lack confidence in autonomous machines and prefer to
be served by a human even if there is a risk of virus transmission. Moreover,
entrusting chatbots to diagnose patients needs a large amount of medical data
from experts. Also, the difference in languages from a country to another
makes an already difficult task still more arduous. On the other hand, when
dealing with voice analysis, there are still many challenges to be taken up.
For example, until now, annotated data of patients’ voices are not publicly
available for research purposes of COVID-19 detection and diagnosis.
Collecting these data is mostly made in unconstrained environments (i.e. in-
the-wild) using smartphones or other voice recorders. These environments are
generally noisy and contain reverberation, which leads to bad quality of data
and makes the diagnosis and detection of COVID-19 more challenging. Finally,
one of the most important future trends is to concentrate on further
decreasing the false negative rate and, as far as practicable, reducing the
false positive rate by the same token to accurately differentiate viral from
BP.

###### Acknowledgements.

The authors would like to thank the ’Agence Nationale de Valorisation des
Résultats de la Recherche et du Développement Technologique (DGRSDT),
Algérie’.

## Conflict of interest

The authors declare that they have no conflict of interest.

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