# COVID-VR: A Deep Learning COVID-19 Classification Model Using Volume-
Rendered Computer Tomography

Noemi Maritza L. Romero, Ricco V. Soares, Mariana R. Mendoza, João L. D. Comba
Instituto de Informática
Universidade Federal do Rio Grande do Sul - UFRGS
Porto Alegre
{nmlromero, ricco.soares, mrmendoza<EMAIL_ADDRESS>

###### Abstract

The COVID-19 pandemic presented numerous challenges to healthcare systems
worldwide. Given that lung infections are prevalent among COVID-19 patients,
chest Computer Tomography (CT) scans have frequently been utilized as an
alternative method for identifying COVID-19 conditions and various other types
of pulmonary diseases. Deep learning architectures have emerged to automate
the identification of pulmonary disease types by leveraging CT scan slices as
inputs for classification models. This paper introduces COVID-VR, a novel
approach for classifying pulmonary diseases based on volume rendering images
of the lungs captured from multiple angles, thereby providing a comprehensive
view of the entire lung in each image. To assess the effectiveness of our
proposal, we compared it against competing strategies utilizing both private
data obtained from partner hospitals and a publicly available dataset. The
results demonstrate that our approach effectively identifies pulmonary lesions
and performs competitively when compared to slice-based methods.

_K_ eywords COVID-19 $\cdot$ Deep Learning $\cdot$ Classification Models
$\cdot$ Computer Tomography $\cdot$ Volume Rendering

##  1 Introduction

The COVID-19 pandemic began spreading extensively in early 2020, resulting in
the unfortunate loss of millions of lives worldwide. Accurate and timely
diagnosis of COVID-19 is crucial, especially during periods of high demand
that can strain healthcare systems. The gold standard method for diagnosing
COVID-19 is the reverse transcriptase-polymerase chain reaction test (RT-PCR)
[1]. However, RT-PCR can be time-consuming and yield false-negative results
[2, 3]. An alternative approach involves analyzing chest images obtained from
X-rays or Computer Tomography (CT) scans [4, 5]. Due to their higher
resolution, CTs can lead to more precise diagnostics.

During the diagnosis process, radiologists search for characteristic patterns
of lung lesions associated with Ground-Glass Opacity (GGO)[6]. GGO appears as
a gray or hazy region of increased attenuation in the lung. In the case of
COVID-19 infection, GGO exhibits specific features such as its location
(peripheral or bilateral), shape (rounded), and appearance (multifocal or
closer to opaque lung tissues known as consolidations). For instance, on top
of Figure1, the GGOs corresponding to COVID-19 are depicted as light magenta
areas within the lungs.

A limitation of CT-based diagnosis is that it consumes around 20 minutes of a
radiologist’s time. To automate this task, machine learning solutions have
emerged, such as Deep Learning (DL) architectures based on Convolutional
Neural Networks (CNNs) that use as input images the slices of a CT [5, 7]. A
slice-based architecture is a common solution since it reflects the local
approach radiologists take when looking at individual slices of a CT. Numerous
proposals have been presented in the literature, and we refer the reader to a
systematic literature review of the most relevant systems [3].

Figure 1: COVID-19 lesions in CT slices and volume renderings of the lung:
(top) axial, coronal, and sagittal CT slices display ground-glass opacities
associated with COVID-19 lesions correspond to light magenta areas inside the
lungs; (bottom) volume rendering from different angles reveal ground-glass
opacities associated with COVID-19 lesions as darker regions inside the lungs

Despite the vast amount of research conducted on this topic, the scientific
community is continuously striving to improve the accuracy and effectiveness
of existing diagnostic models. In this work, we propose an alternative that
follows a global approach of looking at images of the entire lung. We propose
to generate volume-rendering images that use transparency to capture the inner
structures of the lung, taken from different angles to overcome possible
occlusions. The bottom of Figure 1 displays volume rendering images produced
from different angles, in which GGOs are associated with darker regions in the
lung.

Our approach, COVID-VR, is based on a 3D Volume Rendering classification
architecture consisting of a pipeline composed of three main modules. The
first module receives as input a chest CT and performs a segmentation step
that removes material outside the lung. The second module receives a segmented
CT and generates volume rendering of the lung from different angles. The third
module comprises a ResNet architecture [8] that receives volume rendering
images and outputs a classification in two or three classes.

We tested COVID-VR using the COVID-CT-MD
[9]111https://github.com/ShahinSHH/COVID-CT-MD public dataset and a private
dataset obtained from partner hospitals in Porto Alegre (Brazil). We present
classification results comparing our architecture against three competing
strategies, showing that our approach is competitive and can potentially be an
alternative for COVID-19 classification from CTs.

##  2 Related Work

This section summarizes related works relevant to our proposal. First, we
review deep learning-based models described for COVID-19 diagnosis and the
main candidates for comparing the results of our work. Next, we review the use
of volume rendering for a better understanding of COVID-19.

###  2.1 DL-based models for COVID-19 diagnosis from CTs

DL techniques are widely utilized for the classification of CT images. To
develop an effective classification system, it is crucial to comprehend the
patterns associated with each class. Guidelines for classifying COVID-19 based
on CT images can be found in Radiology standards. The Radiology Society of
North America (RSNA) and the British Thoracic Imaging Society (BSTI) have
provided similar classification schemes for COVID-19, consisting of four
categories. The RSNA categories are COVID-19 typical, indeterminate, atypical,
and negative for pneumonia. The typical classification corresponds to patients
with lesions associated with ground-glass opacities, such as those seen in
Figure 1. The indeterminate classification suggests the absence of typical
features, plus multifocal, diffuse, perihilar, nonperipheral, nonrounded, or
unilateral ground-glass opacity with or without consolidation. Atypical
classification denotes the absence of the previous classifications’ features
and the presence of isolated consolidation without ground-glass opacities or
discrete small nodules. The negative classification indicates no features of
pneumonia. Even though both standards describe four classes, the first models
to appear in the literature were either binary (COVID-19 or non-COVID-19) or
ternary (COVID-19, normal, or Community-Acquired Pneumonia (CAP)). The normal
and CAP classes are similar to the negative and indeterminate classes of RSNA.

One way to navigate the extensive literature on the topic is to read the
survey papers that summarize the proposed systems. The survey paper by Hassan
et al. [3] is one of the most complete references on the topic, offering a
categorization of CT-based diagnosis methods (classification, segmentation,
and detection), frameworks and relevant findings and a list of open-source
datasets. In total, they summarize 114 studies.

We summarize below the related work to our proposal that uses CT images as
inputs to their classification models [10, 11, 12, 13, 14, 15, 16, 17, 18].
DeCoVNet [11] uses a weakly-supervised 3D deep convolutional network to
predict the probability of COVID-19 following a binary classification
approach. In the first step, the authors used a U-NET to segment the lung in
the volume and create a 3D binary mask. The CT and its 3D lung mask are sent
to DeCoVNet, which consists of three stages: 3D convolution, 3D residual
blocks, and a progressive classifier. Ternary models soon followed binary
models to allow the separation of COVID-19 from normal cases and other types
of CAPs. An example is COVNet [12], which uses a 3D deep learning framework
that takes 3D slices as input, generates features, combines them using a max-
pooling operation, and finally generates a probability score for each class.
Like DeCoVNet, COVNet also relies on a pre-processing step that uses a U-NET
to create a segmented mask for the lung. Amyar et al. [16] proposes a ternary
model that uses a shared encoder for the classification and lesion
segmentation tasks without requiring labeled segmentation data. Deep-chest
[17] uses a VGG-CNN model that supports classification in four classes:
COVID-19, pneumonia, lung cancer, and normal cases. Serte and Demirel [18]
proposed a binary 3D classification model that fuses image=level predictions
to classify CT volumes. Silveira et al. [19] compute omnidirectional images
from the center of the lungs and use them as inputs to the DL classification
model. Our approach is similar in the sense that it uses volume rendering
images of the lungs, but from an external point of view.

The ICASSP 2021 competition brought an opportunity to compare methods by
providing a public dataset of CTs (COVID-CT-MD [9]) and a contest of
performing classification in three possible classes: normal, COVID-19, or CAP.
The six-best solutions were presented at the ICASSP conference [20, 21, 22,
23, 24, 25]. The first place (team TheSaviours [20]) has an accuracy of 90%.
They describe a two-stage CNN model for detecting COVID-19 and Community-
Acquired Pneumonia (CAP). The first stage is designed to detect infections
(COVID-19 or CAP). The second stage performs the classification into three
classes (CAP, COVID-19, and Normal). The second place (team IITDelhi [21]) has
an accuracy of 88.89%. Their method follows a three-level approach. In the
first level, they use a slice-level classifier that performs feature
extraction from all the slices of the CT to learn different sizes of
infection. The next level performs a patient-level classifier, using four
classifiers to distinguish between infected and normal slices. The last level
uses an ensemble-learning that combines the scores of the previous level
classifiers. The third place (team LLSCP [22]) has an accuracy of 87.78%. They
use a multi-stage progressive learning approach composed of a 3D Resnet
module, an ensemble binary classifier, and a final combining stage. The
remaining entries achieved 85.56% [23], 81.11% [24] and 80% [25].

For comparison purposes, we chose TheSaviours [20] (ICASSP 2021 winner),
DeCoVNet [11], and COVNet [12] approaches due to their outstanding performance
and the public availability of code or pre-trained models.

###  2.2 Volume Rendering Applications in COVID-19

Volume Rendering combines opacity mapping and lighting effects values through
multiple rendering techniques [26, 27, 28]. It is widely used in medical image
visualization [29] to analyze internal spatial relationships between
structures, but few studies report its use in the COVID-19 context.

Tang et al. [30] report one of the first efforts for visualizing COVID-19
pneumonia, displaying lung lesions in a color coronal image and a
tridimensional volume rendering of the lungs, bronchus, and trachea from a 54
years old patient. Li et al. [31] show the advantage of 3D volume rendering to
detect the extent of small pulmonary vessel microangiopathy and alveolar
damage from an autopsy of a COVID-19 patient. COVID-view [32] describes a
system for COVID-19 diagnosis that combines a classification system with
explainable visualizations of activations over 2D slices and volume rendering
of the lungs. They use Maximum Intensity Projection (MIP) to create
projections of volumes that help identify vascular structures. In our work, we
use Volume Rendering instead of MIP, and remains an issue for future work on
how MIP would work in our system. COVID-view user interface allows users to
modify the transfer functions used for volume rendering by choosing from a
preset transfer function or by creating their own. They also use a coronal
clipping tool that allows better inspection of the inner structures inside the
lungs. However, their proposed classification model does not rely on volume
rendering images but uses 2D slices. Similarly, COVIR [33] proposes a
classification model based on 2D slices and a virtual reality platform to
explore 3D reconstructed lungs and segmented infected lesions caused by
COVID-19. The system relies on software such as Blender to fix problems with
the models and Unity to produce the final renderings and support the
interaction through VR devices.

##  3 CT Datasets

This study used COVID-19-related CT datasets from three different sources. The
first source is the Public dataset of CT images prepared for the ICASSP 2021
competition. The other two sources are from partner hospitals in Brazil that
provided CT scans for COVID-19 and non-COVID-19 cases. These datasets are
referred to as Private datasets. CTs for all datasets are given in the DICOM
format. This section describes the details of the creation and statistics of
these datasets.

###  3.1 COVID-CT-MD Public Dataset

The COVID-CT-MD public dataset [9] was released for the SPGC-ICASSP
competition and adopted for model development in other works (_e.g.,_ [20,
21]), thus allowing the comparison of our approach against state-of-the-art
methods. The dataset comprises 307 CT scans with patient-level annotations
divided into three classes: confirmed positive COVID-19 cases, normal cases,
and CAP cases. The COVID-19 cases were collected from February to April 2020,
while CAP and normal cases were collected from April 2018 to December 2019 and
January 2019 to May 2020, respectively. Although slice-level annotations are
available for some patients, we did not explore them in our approach. The
labeled CT scans used for model training and validation are based on a
stratified random split: 30% of these CT scans are randomly selected as the
validation set, and the remaining are used as the train set. Patients are
adults recruited in the Babak Imaging Center, Iran, and exam labeling was
conducted by experienced radiologists [9]. Table 1 shows the number of CT
scans per class.

In addition to the train/validation set, the SPGC-ICCASP competition released
the SPGC-COVID Test Set [34], with four independent test sets for model
evaluation. Three sets were used to calculate the competition results and are
applied for performance comparison of our work with previous approaches. Each
test dataset contains 30 CT scans, as described by Heidarian et al. [34]. The
first set (Test Set 1) comprises COVID-19 and Normal cases (15 and 15,
respectively), obtained from the same image center as the train/validation
set. The second set (Test Set 2) contains the three classes, COVID-19, Normal,
and CAP, with 10 cases for each one, obtained from another imaging center
(Tehran Heart Center, Iran) using different scanner and scanner parameters.
The third set (Test Set 3) contains the three classes, COVID-19, Normal, and
CAP, with ten samples for each one collected in the same imaging center as
Test Set 1. Test Set 2 differs from the others because it includes patients
with a history of cardiovascular diseases and surgeries. The distribution of
samples per class for the SPGC-COVID Test Sets is given in Table 1.

###  3.2 Private Datasets from Partner Hospitals

The private datasets were retrospectively obtained from patients admitted to
two Brazilian hospitals in Porto Alegre: a private institution, Hospital
Moinhos de Vento (HMV), and a public institution, Hospital de Clínicas de
Porto Alegre (HCPA). The Research Ethics Committee in Brazil approved the
study of the participating hospitals, and informed consent was waived due to
the study retrospective nature222HCPA approval - CAAE: 32314720.8.0000.5327,
HMV approval - CAAE: 32314720.8.3001.5330. All data were anonymized by
providers to ensure patient privacy. The HMV dataset contains 284 CT scans
from patients admitted between March and May 2020, whereas the HCPA dataset
comprises 105 CT scans collected from March to June 2020. Both datasets have
patient-level annotations by expert radiologists, following the RSNA standard
[35]. As previously explained (Section 2), the RSNA standard classifies images
into four classes: Typical appearance, Indeterminate appearance, Atypical
appearance, and Negative for pneumonia. The first category corresponds to
exams showing CT features frequently seen in patients with COVID-19 pneumonia
(_e.g.,_ bilateral, peripheral, and multifocal ground-glass opacities), thus
representing our class of interest (_i.e.,_ positive). The distribution of CT
scans among classes is shown in Table 2.

Table 1: COVID-CT-MD (Public dataset): number of CTs and distribution by class. Class |  | Train/Validation
---
(307)
| Test
---
(90)
| F | M | Total | F | M | Total
COVID-19 | 63 | 108 | 171 | 9 | 26 | 35
Normal | 36 | 40 | 76 | 15 | 20 | 35
CAP | 26 | 34 | 60 | 7 | 13 | 20
Table 2: Private dataset: number of CTs and distribution by class. Class |  | HMV
---
(289)
| HCPA
---
(105)
| F | M | Tot. | F | M | Tot.
Typical | 34 | 58 | 92 | 13 | 17 | 30
Negative | 54 | 38 | 92 | 11 | 4 | 15
Indeterm. | 32 | 35 | 67 | 16 | 14 | 30
Atypical | 16 | 17 | 33 | 13 | 17 | 30

##  4 The COVID-VR Classification Architecture

In this section, we describe COVID-VR, an end-to-end pipeline for the analysis
of CT images, from lung segmentation to patient-level classification
associated with the presence of COVID-19 based on volume rendering images
(Figure 2).

Figure 2: The pipeline of COVID-VR has two stages. Stage 1 prepares data to
obtain the input images for model development. In Stage 2, DL models are
trained to distinguish among the classes of interest, and their outputs are
combined to obtain a final patient-level classification.

###  4.1 Lung Segmentation

The first step is to convert the input CT images (Figure 2A) from the DICOM
format to the NIfTI (Neuroimaging Informatics Technology Initiative) format.
We use the ALTIS system [36] to obtain isometric volumes across patients,
which requires interpolating and resizing images with a slice spacing of 1mm
in all dimensions. Several methods for lung segmentation were considered, such
as UBC [37], Lungmask [38], and P-HNN [39]. P-HNN was selected after
recommendations by radiologists who compared the results of these methods.
Lung segmentation is performed with the pre-trained model of P-HNN, using a
probability mask with a threshold of 75% or above to consider a voxel as part
of the lung (Figure 2B).

It is important to observe that our proposal does not require highly-accurate
segmentation results, but enough to discard additional volume around the
lungs. Therefore, we did not perform additional testing to check the accuracy
of the segmentation. The images generated with volume rendering rely on
transfer functions that associate different opacity values that can compensate
for small errors in the segmentation algorithm.

###  4.2 Generating Volume Rendering Images

This step generates volume rendering images from the lung segmented in the
previous stage (Figure 2C). There are two main problems that we need to
address to configure the volume rendering algorithm. The first one is the
specification of a transfer function (TF) that reveals the internal structures
of the lung and lesions associated with COVID-19. The second problem is
choosing the camera positions to generate the volume rendering images.
Ideally, we want to generate images from different points of view that better
capture the lungs and lesions. Both issues are described in this section.

Figure 3: Volume rendering of the lung using customized transfer functions.
TF1 shows the boundary of the lungs but misses internal structures. TF2-TF4
shows internal structures but has similar colors for both GGOs (lesions) and
the lung. TF5-TF6 better separates lungs and lesions.

####  4.2.1 Transfer Function Selection

Volume rendering relies on a transfer function (TF) specification that defines
a mapping from input values to color, transparency, and opacity. The input
values in the segmented lungs are expressed in Hounsfield units (HU), a
dimensionless scale obtained from a linear transformation of the measured
absorption/attenuation of the X-ray beam. Although there is no universal
standard for defining HU intervals for the lungs and GGOs, related works [40,
41] report values of around -700 HU for the lung and in the interval from -700
HU to -300 HU for the GGOs. We use this information to customize TFs.

We use the MITK framework [42] to conduct tests with different TFs and to
create the volume rendering images from the 3D volume of the segmented lungs.
MITK has a user interface that allows for building customized transfer
functions. In our tests, we explored more than 60 different TFs. Figure 3
shows volume rendering images using six different TFs (TF1 to TF6) defined
experimentally. Although they were all applied to the same CT exam, the
rendered image differs significantly. TF1 highlights the outer layer of the
lungs but misses internal structures. TF2 to TF6 reveal internal lesions in
different ways. TF2 is defined within the lower range of values in the lung
and ground-glass opacity (_i.e.,_ [-700, -300]), mapping only HU values in
this interval. This function conserves a particular spatial distribution of
the ground-glass opacity present in the lung but misses details on the lung
texture and regions without any lesions. TF3 aims to replicate the behavior of
2D CTs that vary in a single color scale (usually gray-scale), mapping
original values in the range of [-750, -200] with variations applied to the
brightness. TF3 manages to preserve the lung as a three-dimensional image
without losing critical information on the internal lesions caused by
COVID-19, such as ground-glass opacity. TF4 to TF6 are obtained by including
more colors to help differentiate the lung textures and features of the
different classes. TF4 shows a thin delineation of the lung inner layer and a
prominent highlighting of the bronchi, which in the presence of COVID-19 may
show changes such as bronchial wall thickening [43]. For TF5 and TF6, we
observe a more precise differentiation of ground-glass opacities from other
characteristics, which may be interesting for the classification model as this
feature is the most common imaging finding in COVID-19 patients [43]. Thus, by
exploring a wide range of mapping combinations for color and opacity, each TF
can highlight different regions or features of interest when applied to CT
scans, such as external surfaces of the lungs as shown in TF1, arteries as
shown in TF3, and ground-glass opacity as in TF5 and TF6.

####  4.2.2 Choosing the Viewing Camera Positions

Volume rendering images need to capture the lungs and lesions. An important
issue is the placement of the viewing camera in a location outside the 3D
model of the lung. We explored placing camera positions looking at the main
planes (axial, coronal, and sagittal). We discarded the sagittal plane because
it renders one lung in front of the other. Due to transparency, the volume
rendering of overlapping lungs might lead to combined patterns that might
confuse the classification model.

For axial and coronal planes, we rendered images starting from a position
orthogonal to each plane and rotated the camera position along camera
increments to capture the lungs from slightly different angles. We tested
different angle increments and relied on the results from the classification
model to fine-tune our choices. We captured images at increments of $\pm$1.2°
degrees in the Y and X-axis until reaching a maximum of 12.0° degrees. There
are 21 images on each of the Y and X-axis, leading to 42 different images per
view (_i.e.,_ coronal or axial), for a total of 84 images per dataset.

The resolution of each image was defined to be 448$\times$448px (Figure 2D)
due to the constraints posed by the classification model network. Examples of
the resulting images from different angles are shown in Figure 4.

Figure 4: Volume rendering images used as inputs to the classification model.
The first row represents images extracted for the axial plane. The third image
in this row shows the (0,0) position in which the camera points to the axial
view. From this position, we capture images every 1.2° towards the horizontal
and vertical axis directions, going from -12° to +12° on each axis. The second
row shows the same process for the coronal plane.

###  4.3 Classification Models

COVID-VR is composed of deep neural networks (DNN) models developed using the
Tensorflow framework (Figure 2E). In this section, we detail the architecture
and training of the models, as well as their evaluation.

####  4.3.1 Model Architecture and Training

We use transfer learning to improve models for COVID-19 classification. We
used a pre-trained Convolution Neural Network (CNN) model as the backbone for
the architecture, such as the VGG [44], Resnet [8], DenseNet [45], or
EfficientNet [46]. Figure 2E shows the Resnet101 as the backbone network,
represented in orange. We add a sequence of deep learning modules to this
backbone to help model training. For example, following the backbone, we add a
global average pooling layer, a 20% dropout layer to avoid overfitting, two
fully-connected dense layers, a new 20% dropout layer, and a batch
normalization layer. The final module is a dense layer with a Sigmoid
activation function for the binary classification task and a Softmax
activation function for the ternary classification task. The selection of
which network architecture we would use was performed empirically through
several preliminary experiments. For model compilation, we use the Adam
optimizer with a learning rate of $2e-5$, with a binary (categorical) Cross-
entropy loss function for binary (ternary) models.

Model training and validation were conducted using stratified 5-fold cross-
validation (CV) for the private datasets. We follow the competition
orientation for the public dataset and use a random 70%/30% split from the
COVID-CT-MD dataset [9] to generate the train and validation sets, respecting
original class distributions among splits. The model is further evaluated with
three independent test sets provided by the SPGC-ICASSP competition [34], as
previously described (Section 3.1). Additionally, to increase the size and
variety of the dataset at the training step and reduce the chances of
overfitting, we use data augmentation methods in the training partition, like
rotation up to 15°, zoom ($\pm$5%), rescaling of 1/255, and shift for width
and height images up to 10%.

We train an individual DNN for each defined plane, either axial or coronal,
based on the image views extracted from it. Moreover, despite the use of
patient-level annotations for the supervised learning task, the output of our
DNNs is a classification per image view. Thus, the batch of image views
obtained for a given CT scan (_e.g.,_ 42 axial views or 42 coronal views) is
classified by the corresponding network, specialized in either the axial or
coronal view, generating a class label for each image analyzed. We obtain a
distribution of class votes for each image batch that passes a consensus-
extraction module to generate a patient-level prediction.

Figure 2F illustrates the classification approach. It is defined based on two
submodels, each predicting the COVID-19 diagnosis using images obtained from a
specific view of the reconstructed 3D volume, axial or coronal. We generate a
patient-level consensus distribution based on the distribution of class votes
received from each submodel by summing up the number of votes per class. For
example, in the ternary classification task shown in Figure 2F, the 42 images
generated from the axial view are classified by the corresponding submodel
with the following distribution: 6 votes for class COVID-19, 21 votes for
class CAP, and 15 votes for class Normal. On the other hand, the submodel
trained for coronal view images assigns 36 votes for class COVID-19, three
votes for class CAP, and three votes for class Normal. The ensemble-based
solution generated from the sum of submodels votes results in 42 images
classified as COVID-19, 24 images classified as CAP, and 18 images classified
as Normal. Figure 2G shows the final model used to predict a single label for
the input CT scan and the class with the maximum number of votes according to
both views. In this example, the CT would be classified as COVID-19 by our
approach.

####  4.3.2 Model Evaluation

We use the following evaluation metrics to assess the performance of our
COVID-19 models: accuracy (Acc), sensitivity (Sens, also called recall),
specificity (Spec), precision (Prec), F1-score (_i.e.,_ the harmonic mean
between recall and precision), and the area under the Receiver Operating
Characteristic (ROC) Curve (AUC score). Given its multiclass nature in the
ternary model, we adopted the micro-and macro-average for all metrics except
accuracy.

##  5 Results

This section reports the evaluation of the models proposed in COVID-VR. We
first compare various backbone networks and transfer functions using the
public dataset (COVID-CT-MD). We use the insights from these results in
subsequent experiments. Next, we compare the classification performance of the
models with state-of-the-art approaches for detecting COVID-19-related
pneumonia in the public dataset. Finally, we discuss the performance of the
models developed from the private datasets and compare both ternary and binary
classification models. Since there is a class imbalance in the datasets, we
focus our discussion on the micro-average metric for the ternary models. Full
results are provided in Table 5 and Table 8.

###  5.1 Selection of the Backbone Network

We tested several CNN architectures as the backbone network to perform CT scan
classification with the models described in Section4.3, including ResNet [8],
DenseNet [45], VGG [44], and EfficientNet [46] families. We explored different
depths for each architecture (_i.e.,_ ResNet50, ResNet101, DenseNet121,
DenseNet201, EfficientNet-B0, EfficientNet-B1, EfficientNet-B6, VGG16, and
VGG19) with fixed training and validation sets contained in the COVID-CT-MD
dataset. A comparison of the classification performance of the better models
for each network family is given in Table 3. We use a fixed transfer function
(TF6) to generate volume rendering images in all experiments. The choice of
the transfer function is explained in the next section. The performance
results correspond to the ternary classification models using the train and
validation sets from the public dataset (_i.e.,_ COVID-CT-MD dataset). We show
the performance for the COVID-19 class and the overall performance for the
three classes. F1 and AUC scores are obtained with the micro-average.
Additional results per class can be found in Table 5.

Table 3: Comparison of distinct backbone network architectures in COVID-VR. Model training and validation use the train and validation sets from the COVID-CT-MD public dataset for the ternary classification task (COVID-19 vs. CAP vs. Normal). | Metrics | VGG16 | DenseNet121 | EfficientNet-B2 | ResNet101
---|---|---|---|---|---
Overall | Acc | 88.8% | 87.8% | 86.7% | 90.8%
F1 | 88.6% | 87.7% | 86.7% | 90.8%
AUC | 95.6 | 96.5 | 95.1 | 95.4
COVID-19 | Sens | 89.1% | 85.5% | 83.6% | 89.1%
Spec | 88.4% | 93.0% | 90.7% | 93.0%
Prec | 90.7% | 94.0% | 92.0% | 94.2%
F1 | 89.9% | 89.5% | 87.6% | 91.6%

Our ternary model achieved the best results using the ResNet101 network as its
backbone. The validation accuracy and F1-score were the highest among the best
models for each network family. Both accuracy and F1-score for the overall
classification in a ternary approach were 90.8%, approximately two percentage
points above VGG16, which presented the second-best performance (Table 3).
Regarding AUC score, DenseNet121 achieved the highest mark of 96.5 compared to
95.4 for ResNet101. Nonetheless, observing the classification results for the
COVID-19 class (Table 3), we note that ResNet101 showed the best performance
for all metrics. Moreover, it achieved the best performance for CAP cases and
competitive performance for Normal cases (see Table 5). Thus, we chose
ResNet101 as the backbone network for COVID-VR, using it for all the
subsequent experiments.

###  5.2 Selection of the Transfer Function

We compared the transfer functions discussed in Section 4.2.1 to decide which
one we would use to generate volume rendering images. We used the results of
the ternary classification model for this purpose, with the train and
validation sets from the public dataset. Table 4 summarizes the results. TF4
and TF5 were omitted due to similar performance obtained with TF3 and TF2,
respectively. TF6 presented the best accuracy and overall F1 score
performance. Although TF6 does not have the highest sensitivity, it had a
balanced performance in detecting COVID-19 cases. In addition, TF6 achieved
the best sensitivity for CAP and normal cases.

Figure 5 compares the ROC curves obtained for distinct TFs. Although there is
a small between the AUC score for TF6 and TF2, the ROC curve for TF6 achieves
the highest sensitivity for a 10% false-positive rate. Therefore, we chose TF6
as the standard transfer function. Besides the visual detection of different
features highlighted by each TF, this test proved relevant, as switching the
TF while keeping the same model architecture results in considerable accuracy
variation.

Table 4: Comparison of distinct transfer functions in COVID-VR. Model training and validation use the train and validation sets from the COVID-CT-MD public dataset for the ternary classification task (COVID-19 vs. CAP vs. Normal). | Metrics | TF1 | TF2 | TF3 | TF6
---|---|---|---|---|---
Overall | Acc | 79.6% | 85.7% | 87.8% | 90.8%
F1 | 78.7% | 84.5% | 87.6% | 90.8%
AUC | 91.8 | 96.3 | 94.7 | 95.4
COVID-19 | Sens | 89.1% | 96.4% | 89.1% | 89.1%
Spec | 72.1% | 72.1% | 88.4% | 93.0%
Prec | 80.3% | 81.5% | 90.7% | 94.2%
F1 | 84.5% | 88.3% | 89.9% | 91.6%
Figure 5: ROC curves for transfer functions TF1, TF2, TF3, and TF6

###  5.3 Classification Performance for the Public Dataset

We conducted experiments to evaluate COVID-VR for the ternary classification
model (COVID-19 vs. CAP vs. Normal) in the validation set and the three test
sets from the COVID-CT-MD dataset (here unified in a single test set). All
models use the ResNet101 backbone network and TF6 for volume rendering. We
compared COVID-VR against the winner of the competition (TheSaviours) [20],
and two state-of-the-art methods, DeCoVNet [11] and COVNet [12].

Table 5: Ternary classification results by class in the Public COVID-CT-MD.
Performance metrics are in percentage.

|  |  |  | By Classes
---|---|---|---|---
|  | General | COVID-19 | Normal | CAP
|  | Accu. | Kappa | Preci. | Sensi. | Speci | F-score | Preci. | Sensi. | Speci. | F-score | Preci. | Sensi. | Speci. | F-score
| TF 1 | 79.6 | 63.7 | 80.3 | 89.1 | 72.1 | 84.5 | 71.4 | 83.3 | 89.2 | 76.9 | 100.0 | 47.4 | 100.0 | 64.3
| TF 2 | 85.7 | 74.0 | 81.5 | 96.4 | 72.1 | 88.3 | 95.7 | 91.7 | 98.6 | 93.6 | 90.0 | 47.4 | 98.7 | 62.1
| TF 3 | 87.8 | 79.2 | 90.7 | 89.1 | 88.4 | 89.9 | 85.2 | 95.8 | 94.6 | 90.2 | 82.4 | 73.7 | 96.2 | 77.8
| TF 4 | 88.8 | 80.8 | 90.9 | 90.9 | 88.4 | 90.9 | 85.2 | 95.8 | 94.6 | 90.2 | 87.5 | 73.7 | 97.5 | 80.0
| TF 5 | 85.7 | 75.4 | 87.5 | 89.1 | 83.7 | 88.3 | 82.1 | 95.8 | 93.2 | 88.5 | 85.7 | 63.2 | 97.5 | 72.7
Transfer Function Comparison | TF 6 | 90.8 | 84.6 | 94.2 | 89.1 | 93.0 | 91.6 | 85.7 | 100.0 | 94.6 | 92.3 | 88.9 | 84.2 | 97.5 | 86.5
| VGG16 | 88.8 | 81.0 | 90.7 | 89.1 | 88.4 | 89.9 | 88.9 | 100.0 | 95.9 | 94.1 | 82.4 | 73.7 | 96.2 | 77.8
| DenseNet121 | 87.8 | 79.7 | 94.0 | 85.5 | 93.0 | 89.5 | 82.8 | 100.0 | 93.2 | 90.6 | 78.9 | 78.9 | 94.9 | 78.9
| EfficientNetB2 | 86.7 | 78.0 | 92.0 | 83.6 | 90.7 | 87.6 | 85.7 | 100.0 | 94.6 | 92.3 | 75.0 | 78.9 | 93.7 | 76.9
Architecture Comparison | ResNet101 | 90.8 | 84.6 | 94.2 | 89.1 | 93.0 | 91.6 | 85.7 | 100.0 | 94.6 | 92.3 | 88.9 | 84.2 | 97.5 | 86.5
| DeCovNet | 67.3 | 44.9 | 74.5 | 74.5 | 67.4 | 74.5 | 71.4 | 41.7 | 94.6 | 52.6 | 51.7 | 78.9 | 82.3 | 62.5
| COVNet | 77.6 | 63.2 | 85.4 | 74.5 | 83.7 | 79.6 | 66.7 | 83.3 | 86.5 | 74.1 | 75.0 | 78.9 | 93.7 | 76.9
| TheSaviours | 74.5 | 58.8 | 86.7 | 70.9 | 86.0 | 78.0 | 51.4 | 75.0 | 77.0 | 61.0 | 88.9 | 84.2 | 97.5 | 86.5
Method Comparison (Train / Validation) | COVID-VR | 90.8 | 84.6 | 94.2 | 89.1 | 93.0 | 91.6 | 85.7 | 100.0 | 94.6 | 92.3 | 88.9 | 84.2 | 97.5 | 86.5
| DeCovNet | 52.2 | 30.3 | 48.8 | 57.1 | 61.8 | 52.6 | 77.8 | 20.0 | 96.4 | 31.8 | 50.0 | 100.0 | 71.4 | 66.7
| COVNet | 67.8 | 50.0 | 60.0 | 77.1 | 67.3 | 67.5 | 74.1 | 57.1 | 87.3 | 64.5 | 77.8 | 70.0 | 94.3 | 73.7
| TheSaviours | 90.0 | 84.6 | 90.9 | 85.7 | 94.5 | 88.2 | 89.2 | 94.3 | 97.1 | 90.0 | 90.0 | 90.0 | 90.0 | 94.4
Method Comparison (Test) | COVID-VR | 86.7 | 79.7 | 89.2 | 94.3 | 92.7 | 91.7 | 96.4 | 77.1 | 98.2 | 85.7 | 72.0 | 90.0 | 90.0 | 80.0

Table 5 summarizes the results. COVID-VR achieved the highest overall
performance in the validation set. COVID-VR accuracy was 90.8% in contrast to
77.6% obtained with COVNet, which ranked second. In terms of AUC score, COVID-
VR achieved 95.4 while COVNet achieved 89.8. COVID-VR also had the best
predictive power for COVID-19 cases, reaching high and balanced sensitivity
and specificity values. When comparing the CAP and Normal cases, COVID-VR had
a 100% sensitivity and 94% specificity for the Normal class and 84.2%
sensitivity and 97.5% specificity for the CAP class - in both cases surpassing
the competing strategies.

COVID-VR achieved an accuracy of 86.7% in the test set, while TheSaviours
correctly classified 90.0% of the test instances. COVID-VR was the best method
for detecting COVID-19 cases (94.3% sensitivity), keeping high specificity
(92.7%), and F1-score (91.7%). In contrast, TheSaviours achieved the highest
F1-score for Normal and CAP cases. The COVID-VR model is trained with labels
at the patient level, while the approach presented by TheSaviours [20] trains
a model by exploring labels at the slice level. We reach similar results
despite using a coarser-grained annotation in CT scans.

Figure 6 compares the micro-average ROC curves for the ternary classification
models using the validation test (top) and the test set (bottom). COVID-VR has
the best performance in the validation set, notably improving the true
positive rate (_i.e.,_ sensitivity) for false-positive rates ranging from 0 to
0.3. Considering the test set, the performance of the TheSaviours model
improves in experiments with the validation set and surpasses COVID-VR
(_i.e.,_ 97.4 vs. 95.7) in the AUC score. Nonetheless, we highlight that
COVID-VR had the most stable performance between validation and test sets,
despite the clinical and technical differences introduced in the CT images
from the SPGC-COVID Test Set [34]. Finally, we note that COVID-VR had an
accuracy close to that reported by the first place of the competition like
IITDelhi [21] with 88.9%, LLSCP [22] with 87.8%, and UniSheff_EEE [23] with
85.56%. The results for these approaches were not included in the table due to
the lack of public code to reproduce the experiments.

Figure 6: Micro-average ROC curves for the ternary classification task using
the public dataset: validation set (left) and test set (right).

Figure 7: ROC curves for the COVID-19 against the class others (left) and
normal (right) in the private dataset.

###  5.4 Classification Performance for the Private Dataset

We analyzed the performance of the methods for a binary classification task,
COVID-19 vs. non-COVID-19, using the private datasets. The experiments
considered two definitions for the negative class: in the first, we merged the
Negative, Indeterminate, and Atypical classes into a unique non-COVID-19
class, and in the second, we only considered the original negative class
(_i.e.,_ Negative for pneumonia) as the classifiers non-COVID-19 class. In
both cases, the Typical classification was considered the positive class
(_i.e.,_ COVID-19). Performance assessment was based on a 5-fold CV, with the
configuration of the same folds for COVID-VR, DeCoVNet, and COVNet. COVID-VR
obtained the best results in all metrics, with 92.2% of accuracy and 95.6% of
AUC for this binary classification task (Table 6). In comparing COVID-19 vs.
Normal classification, COVID-VR obtained accuracy and F1-score similar to
COVNet, but with higher Sensitivity and AUC scores (Table 7). The ROC curves
are given in Figure 7, that shows that in both cases COVID-VR has the best AUC
scores.

Table 6: COVID-19 vs. Others task results. Training and validation technique in the private (HMV+HCPA) dataset  Metrics | COVID-VR | DeCovNet | COVNet
---|---|---|---
Acc | 92.2% | 87.8% | 89.4%
Sens | 83.6% | 78.7% | 83.6%
Spec | 96.2% | 92.0% | 92.0%
F1 | 87.2% | 80.3% | 83.3%
AUC | 95.6 | 89.2 | 93.1
Table 7: COVID-19 vs. Normal task results. Training and validation technique in the private (HMV+HCPA) dataset  Metrics | COVID-VR | DeCovNet | COVNet
---|---|---|---
Acc | 96.1% | 92.5% | 96.1%
Sens | 96.7% | 91.0% | 95.9%
Spec | 95.3% | 94.3% | 96.2%
F1 | 96.3% | 92.9% | 96.3%
AUC | 98.6 | 96.3 | 97.7
Figure 8: Grad-CAM visual activations for COVID-VR using a COVID-19 patient
from COVID-CT-MD dataset. The first column displays the volume rendering
image. The second column shows heatmaps indicating which areas from the input
image most activated the model for the class COVID-19, leading it to correctly
classify that patient. In contrast, the second and third column show the
heatmaps of activation maps for Normal and CAP class respectively. The first
and last rows show thumbnails of all the activation maps.

###  5.5 Visual Explanation of Activations using Grad-CAM

We generated Grad-CAM images [47] to explain the learning of the COVID-VR
model. Figure 8 shows the last convolutional layer activation heatmaps for the
COVID-VR ternary model (COVID-19, Normal, and CAP) over the axial and coronal
view of a COVID-19 patient. The GRAD-CAM uses the default jet colormap. Since
The COVID-VR model classifies 42 images (rotated by small angles) per patient
and view, each image generates an activation map for each class, illustrated
in Figure 8 as thumbnail images. The axial and coronal views show the mean of
activation maps for each class from the central point of view of the camera.

The Grad-CAM heatmaps show what most influenced the classification. The
COVID-19 class corresponds to red areas with lesions in the axial and coronal
views. The Normal class activates almost the entire image, avoiding one of the
main lesions from COVID-19 activation map in axial and coronal views and other
visible lesions in the coronal view. Almost the entire lung is activated in
the CAP class but less intense than in the Normal class.

Table 8: Micro and macro average values for the Public Dataset ICASSP-SPGC Competition. Performance metrics are in percentage |  | Micro-average | Macro-average
---|---|---|---
|  | Preci. | Sensi. | Speci. | F-score | AUC | Preci. | Sensi. | Speci. | F-score | AUC
| TF 1 | 81.9 | 79.6 | 81.7 | 78.7 | 91.8 | 86.1 | 86.2 | 93.1 | 86.0 | 94.1
| TF 2 | 86.6 | 85.7 | 83.7 | 84.5 | 96.3 | 89.1 | 78.5 | 89.8 | 81.3 | 95.2
| TF 3 | 87.7 | 87.8 | 91.4 | 87.6 | 94.7 | 83.9 | 73.3 | 87.1 | 75.2 | 90.2
| TF 4 | 88.8 | 88.8 | 91.7 | 88.6 | 95.2 | 87.9 | 86.8 | 93.5 | 87.0 | 94.7
| TF 5 | 85.8 | 85.7 | 88.7 | 85.3 | 95.0 | 85.1 | 82.7 | 95.1 | 83.2 | 95.2
Transfer Function Comparison | TF 6 | 91.1 | 90.8 | 94.3 | 90.8 | 95.4 | 89.6 | 91.1 | 95.0 | 90.1 | 95.2
| VGG16 | 88.6 | 88.8 | 91.7 | 88.6 | 95.4 | 87.3 | 87.6 | 93.5 | 87.3 | 94.9
| DenseNet121 | 88.3 | 87.8 | 93.4 | 87.7 | 96.5 | 85.2 | 88.1 | 93.7 | 86.3 | 95.7
| EfficientNetB2 | 87.2 | 86.7 | 92.2 | 86.7 | 95.1 | 84.2 | 87.5 | 93.0 | 85.6 | 94.0
Architecture Comparison | ResNet101 | 91.1 | 90.8 | 94.3 | 90.8 | 95.4 | 89.6 | 91.1 | 95.0 | 90.1 | 95.2
| DeCovNet | 69.3 | 67.3 | 76.9 | 66.8 | 78.2 | 65.9 | 65.0 | 81.4 | 63.2 | 76.8
| COVNet | 78.8 | 77.5 | 86.3 | 77.7 | 89.8 | 75.7 | 78.9 | 88.0 | 76.9 | 89.3
| TheSaviours | 78.5 | 74.5 | 86.0 | 75.5 | 88.4 | 75.7 | 76.7 | 86.8 | 75.2 | 87.7
Method Comparison (Train / Validation) | COVID-VR | 91.1 | 90.8 | 94.3 | 90.8 | 95.4 | 89.6 | 91.1 | 95.0 | 90.1 | 95.2
| DeCovNet | 60.3 | 52.2 | 77.4 | 47.6 | 78.7 | 58.9 | 59.0 | 76.5 | 50.4 | 78.7
| COVNet | 69.4 | 67.7 | 81.1 | 67.7 | 86.1 | 70.6 | 68.1 | 83.0 | 68.6 | 86.6
| TheSaviours | 90.0 | 90.0 | 94.4 | 90.0 | 97.4 | 90.0 | 90.0 | 94.8 | 90.0 | 98.0
Method Comparison (Test) | COVID-VR | 88.2 | 86.7 | 94.2 | 86.8 | 95.7 | 85.9 | 87.1 | 93.6 | 85.8 | 96.1

##  6 Discussion

In summary, the main results showed that the COVID-VR architecture reached an
accuracy of 90.8% and an F1-score of 90.8% in ternary classification using the
COVID-CT-MD [9] public dataset. The binary classification of COVID-19 vs.
others (Negative, Indeterminate, and Atypical CT images) achieved an accuracy
of 92.2% with an F1-score of 87.2%. Finally, COVID-VR had an accuracy of 96.1%
with an F1-score of 96.3% in the binary classification COVID-19 vs. Normal
(Negative) task using COVID-19 class as the positive class in the private
datasets given by partner hospitals.

The experiments suggest that COVID-VR achieves the goal of learning to
recognize typical COVID-19 patterns in chest CT images in comparison to other
competing strategies. The COVID-VR model can help specialists in the COVID-19
diagnosis by performing a binary classification that identifies or discards
typical cases of COVID-19. Although TheSaviours model leads the performance
for the ternary classification model, COVID-VR has competitive results and
does not require labeling lesions in slides. In summary, COVID-VR reveals the
potential of using images from the exterior of the lungs in comparison to
slices used in traditional approaches.

To allow our proposal to be reproduced and compared against other proposals,
we made the COVID-VR model available at https://github.com/covid-vr/covid-vr-
docker.

##  7 Conclusion and Future Work

In this work, we introduced COVID-VR, a novel 3D Volume Rendering
classification architecture designed for classifying pulmonary diseases using
volume-rendering images of the lungs. The architecture consists of three main
modules: segmentation, volume rendering, and classification. The segmentation
module removes non-lung material from the input chest CT scan, while the
volume rendering module generates lung images from various angles. Unlike
slice-based approaches that rely on images from specific CT slices (axial,
coronal, or sagittal), the volume rendering technique offers a comprehensive
view of the entire lung in each image, overcoming potential occlusions.
Transparency is used to render the inner structures of the lung, and images
are generated from angles that capture different views of the lung. Finally,
the classification module employs a ResNet architecture to classify the volume
rendering images into two or three classes (COVID-19, CAP and normal).

To evaluate the effectiveness of our approach, we conducted experiments using
a publicly available COVID-CT-MD dataset and a private dataset from partner
hospitals. The classification results are compared against competing
strategies, demonstrating that COVID-VR achieves competitive classification
results without requiring the labeling of lesions in individual slides.

We recognize that there is still room for improvement in the COVID-VR model.
One area to explore further is the generation of transfer functions using deep
learning methods. Additionally, we aim to investigate the application of
volume-rendered images in other classification scenarios, expanding the
potential of our approach beyond pulmonary diseases.

## Acknowledgments

This work was partially financed by the Coordenação de Aperfeiçoamento de
Pessoal de Nível Superior - Brasil (CAPES) - Finance Code 001, FAPERGS
20/2551-0000254-3 and CNPq 140313/2017-6.

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