=Paper= {{Paper |id=Vol-2742/paper3 |storemode=property |title=Understanding Automatic COVID-19 Classification Using Chest X-ray images |pdfUrl=https://ceur-ws.org/Vol-2742/paper3.pdf |volume=Vol-2742 |authors=Pierangela Bruno,Cinzia Marte,Francesco Calimeri |dblpUrl=https://dblp.org/rec/conf/aiia/BrunoMC20 }} ==Understanding Automatic COVID-19 Classification Using Chest X-ray images== https://ceur-ws.org/Vol-2742/paper3.pdf

Understanding Automatic COVID-19
Classification using Chest X-ray images

Pierangela Bruno1[0000−0002−0832−0151] , Cinzia Marte1[0000−0003−3920−8186] , and
Francesco Calimeri1[0000−0002−0866−0834]

Department of Mathematics and Computer Science,
University of Calabria, Rende, Italy
{bruno,marte,calimeri}@mat.unical.it

Abstract. The COVID-19 disease caused by the SARS-CoV-2 virus
first appeared in Wuhan, China, and is considered a serious disease due
to its high permeability, and contagiousness. The similarity of COVID-
19 disease with other lung infections, along with its high spreading rate,
makes the diagnosis difficult. Solutions based on machine learning tech-
niques achieved relevant results in identifying the correct disease and
providing early diagnosis, and can hence provide significant clinical de-
cision support; however, such approaches suffer from the lack of proper
means for interpreting the choices made by the models, especially in case
of deep learning ones. With the aim to improve interpretability and ex-
plainability in the process of making qualified decisions, we designed a
system that allows a partial opening of this black box by means of proper
investigations on the rationale behind the decisions. We tested our ap-
proach over artificial neural networks trained for multiple classification
based on Chest X-ray images; our tool analyzed the internal processes
performed by the networks during the classification tasks to identify the
most important elements involved in the training process that influence
the network’s decisions. We report the results of an experimental analysis
aimed at assessing the viability of the proposed approach.

Keywords: GradCAM · Chest X-ray images · Convolutional Neural
Networks

1 Introduction

The Novel Coronavirus, that reportedly started to infect human individuals at
the end of 2019, rapidly caused a pandemic, as the infection can spread quickly
from individual to individual in the community [16]. Signs of infection include
respiratory symptoms, fever, cough and dyspnea. In more serious cases, the in-
fection can cause Pneumonia, severe acute respiratory syndrome, septic shock,
multi-organ failure, and death [15, 13].
Early and automatic diagnoses are relevant to control the epidemic, paving
the way to timely referral of patients to quarantine, rapid intubation of serious
Copyright c 2020 for this paper by its authors. Use permitted under Creative Com-
mons License Attribution 4.0 International (CC BY 4.0)
cases in specialized hospitals, and monitoring of the spread of the disease. Since
the disease heavily affects human lungs, analyzing Chest X-ray images of the
lungs may prove to be a powerful tool for disease investigation. Several methods
have been proposed in the literature in order to perform disease classification
from Chest X-ray images, especially based on deep learning approaches [29,
9, 4]. Notably, in this context, solutions featuring interpretability and explain-
ability approaches can significantly help at improving disease classification and
providing context-aware assistance and understanding. Indeed, interpreting the
decision-making processes of neural networks can be of great help at enhanc-
ing the diagnostic capabilities and providing direct patient- and process-specific
support to diagnosis and surgical tool detection. However, interpretability and
explainability represent critical points in approaches based on deep learning
models, that achieved great results in disease classification.
In this work, we investigate the use of convolutional neural networks (CNNs)
with the aim to perform multiple-disease classification from Chest X-ray images.
Diseases that are a matter of concern for our experiments are COVID-19, Vi-
ral Pneumonia and Streptococcus Pneumonia. Notably, although these diseases
are characterized by pulmonary inflammation caused by different pathogens,
Streptococcus Pneumonia and Viral Pneumonia have similar clinical symptoms
to COVID-19 [24, 28], such as fever, chills, cough, and dyspnea. The symptom-
based similarity among diseases is a critical factor that could affect a proper
diagnosis and treatment plan. Moreover, we include in our experiments Healthy
patients to learn how they differ from symptomatic patients.
We analyze the CNNs-based model to identify the mechanisms and the moti-
vations steering neural networks decisions in classification task. In particular, we
use gradient visualization techniques to produce coarse localization maps high-
lighting the image regions most likely to be referred to by the model when the
classification decision is taken. The highlighted areas are then used to discover
(i) patterns in Chest X-ray images related to a specific disease, and (ii) corre-
lation between these areas and classification accuracy, by analyzing a possible
performance worsening after their removal.
The remainder of the paper is structured as follows. We first briefly report on
related work in Section 2; in Section 3 we then provide a detailed description of
our approach, that has been assessed via a careful experimental activity, which
is discussed in Section 4; we analyze and discuss results in Section 5, eventually
drawing our conclusions in Section 6.

2 Related work

In this section we present state-of-the-art methods used to (i) perform disease
classification through Chest X-ray images and (ii) provide interpretability and
explainability of the rationale behind the decisions performed.
Disease Classification. Deep learning-based models recently achieved promis-
ing results in image-based disease classification. These models, such as CNNs [14,
6, 20, 25], are proven to be appropriate and effective when compared to conven-
tional methods; indeed, CNNs currently represent the most widely used method
for image processing. Abbas et al. [1] proposed a deep learning approach (De-
TraC) to perform disease classification using X-ray images. The approach was
used to distinguish COVID-19 X-ray images from normal ones, achieving an
accuracy of 95.12%. An improvement in terms of binary classification accuracy
was presented by Ozturk et al. [17]. The authors proposed a deep learning model
(DarkCovidNet) for automatic diagnosis of COVID-19 based on Chest X-ray im-
ages. They both performed a binary and multiclass classification, dealing with
patients with COVID-19, no-findings and Pneumonia. The accuracy achieved
is of 98.08% and 87.02%, respectively. Similarly, Wang et al. [22] proposed a
deep learning-based approach (COVID-Net) to detect distinctive abnormalities
in Chest X-ray images among patients with non-COVID-19 viral infections, bac-
terial infections, and healthy patients, achieving an overall accuracy of 92.6%.
All the approaches showed limitations related to low number of image samples
and imprecise localization on the chest region. More accurate localization of
model’s prediction was proposed by Mangal et al. [11] and Haghanifar et al. [7].
The authors proposed a deep learning-based approach to classify COVID-19 pa-
tients from others/normal ones. They also generated saliency maps to show the
classification score obtained during the prediction and to validate the results.
Explainability of deep learning model. In the last year, attempts at
understanding neural networks decision-making have raised a lot of interest in
the scientific community. Several approaches have been proposed to visualize the
behavior of a CNN by sampling image patches that maximize the activation of
hidden units [26], and by backpropagation to identify or generate salient image
features [10]. Other researchers were trying to solve this problem by explaining
neural network decisions by generating informative heatmaps such as Gradient-
weighted Class Activation Mapping (GradCAM) [19, 3], or through layer-wise
relevance propagation [2]. However, these methods present some limitations; in-
deed, the generated heatmaps were basically qualitative, and not informative
enough to specify which concepts have been detected. An improvement was
provided using semantically explanation from visual representation [27] to de-
compose the evidence for a prediction for image classification into semantically
interpretable components, each with an identified purpose, a heatmap, and a
ranked contribution.
In this work, we propose the use of Deep Learning approach to perform mul-
tiple disease classification using Chest X-ray. Additionally, we take advantage of
a novel technique for analyzing the internal processes and the decision performed
by a neural network during the training phase.

3 Proposed Approach
3.1 Classification
Considering that other diseases appear similar to COVID-19 Pneumonia, in-
cluding other Coronavirus infections and community-acquired Pneumonia such
Fig. 1: Workflow of the proposed framework. Chest X-ray images are used to train
the CNN. The last convolution layer of the CNN is used as input of the GradCAM
approach to provide the corresponding visual explanations (i.e the regions of input that
are “important” for classification capability).

Table 1: Architecture of the networks DenseNet-121, DenseNet-169 and DenseNet-201.
More in detail, Conv stands for convolution, DB for Dense Block, TL for Transition
Layer, CL for Classification Layer.

Fig. 2: Architecture of the network Inception-v3.
as Streptococcus, the distinction between these is extremely important and nec-
essary, especially during a pandemic. Therefore, our purpose is to automatically
identify the “correct” disease in Chest X-ray images.
In order to achieve this goal, we train CNN to classify patients according to
3 similar-based symptoms diseases (i.e., COVID-19, Viral Pneumonia, Strepto-
coccus Pneumonia) and Healthy patients.
The herein proposed approach, illustrated in Fig. 1, is based on: (i) Multiple-
disease classification using CNNs, and (ii) Visual Explanations using GradCAM
to indicate the discriminative image regions used by the CNN.
In order to classify patients, we used and compared the results of four neural
networks chosen on the basis of the good performance obtained on the Ima-
geNet data set over several competitions [18]. We make use of DenseNet 121,
DenseNet 169, DenseNet 201 and Inception V3 [21].
DenseNet networks [8] are made of dense blocks, as shown in Table 1, where
for each layer the inputs are the feature maps of all the previous layers with
the aim to improve the information flow on 224 × 224 input images. More in
detail, for convolutional layers with kernel size 3 × 3, each side of the inputs is
zero-padded by one pixel to keep the feature-map size fixed. The layers between
two contiguous dense blocks are referred as transition layers for convolution and
pooling, which contain 1 × 1 convolution and 2 × 2 average pooling. A 1 × 1
convolution is introduced as a bottleneck layer before each 3 × 3 convolution to
reduce the number of input feature-maps, and thus to improve computational
efficiency. At the end of the last dense block, a global average pooling and a
softmax classifier are applied.

The structure of Inception-v3 is shown in Fig. 2. The Inception modules
(Inception A, Inception B and Inception C) are well-designed convolution mod-
ules that can both generate discriminatory features and reduce the number of
parameters. Each Inception module is composed of several convolutional layers
and pooling layers in parallel. The network is composed of 3 Inception A mod-
ules, 5 Inception B modules, and 2 Inception C modules that are stacked in
series. The input size used is 224 × 224 and, after the Inception modules and
convolutional layers, the feature map dimensions were 5 × 5 with 2.048 chan-
nels. Afterwards, we added 3 fully connected layers to the end of the Inception
modules, and, finally, a softmax layer was added as a classifier outputting a
probability for each class, and the one with the highest probability was chosen
as the predicted class.

3.2 Visual Explanations
We used GradCAM to identify visual features in the input able to explain re-
sult process achieved during the multiple classification. The overall structure of
GradCAM is showed in Fig. 3. In particular, it uses the gradient information
flowing into the last convolutional layer of the CNN to assign importance values
to each neuron. GradCAM is applied to a trained neural network with fixed
weights. Given a class of interest c, let y c the raw output of the neural network,
Fig. 3: An example of GradCAM structure. Given an image and a category (”Diagnosis
c”) as input, we foward-propagate the image through the model to obtain the raw class
scores before softmax. The gradients are set to zero for all classes, except for desired
class (”Diagnosis c”), which is set to 1. This signal is then backpropagated to the
rectified convolutional feature map (A) of interest, where we can compute the coarse
GradCAM localization (blue heatmap).

that is, the value obtained before the application of softmax used to trasform
the raw score into a probability. GradCAM performs the following three steps:
1. Compute Gradient of yc w.r.t. featurec maps activation Ak , for any arbi-
δy
trary k, of a convolutional layer (i.e., δA k ). This gradient value depends on

the input image chosen; indeed, the input image determines both the feature
maps Ak and the final class score y c that is produced.
2. Calculate Alphas by Averaging Gradients over the width dimension
(indexed by i) and the height dimension (indexed by j) to obtain neuron
importance weights αkc , as follows:
global average pooling
zX}|X{ c
1 δy
αkc = ,
Z i j
δAki,j
| {z }
gradients via backprop

where Z is a constant (i.e., number of pixels in the activation map).
3. Calculate Final GradCAM Heatmap by performing a weighted combi-
nation of the feature map activations Ak as follows:
X
LcGradCAM = ReLU ( αkc Ak ),
k
| {z }
linear combination

where αkc is a different weight for each k, and ReLU is the Rectified Linear
Unit operation used to emphasize only the positive values and to convert
the negative values into 0.
(a) COVID-19 (b) Viral (c) Streptococcus (d) Healthy
Pneumonia Pneumonia patients

Fig. 4: Example of frontal-view chest X-ray images for the treated pathologies.

4 Experimental Protocol
We describe next the setting of the experimental analysis performed in order to
assess the viability of our approach.

4.1 Dataset description
For the experimental analysis we used datasets provided by Cohen et al. [5] and
Wang et al. [23]. The datasets consist of several X-ray extracted from various
online publications and websites. Examples of X-ray images are shown in Fig. 4.
The datasets specifically include images of COVID-19 cases along with others,
such as, for instance, Viral and Bacterial Pneumonia based images.
In particular, we considered only 4 specific categories distributed as follows:
1. COVID-19, counting 434 patients
2. Viral Pneumonia, counting 1337 patients
3. Streptococcus Pneumonia, counting 1400 patients
4. Healthy patients, counting 1341 patients
In order to obtain a valid classification and avoid majority class selection, we
properly made use of data augmentation techniques to over-sample imbalanced
data an obtain an equal number of samples in abundant class. More specifically,
we performed:
• Translating medical images: shift the region of interest with respect to the
center of the training images;
• Rotating medical images: rotate the training images by a random amount of
degrees;
• Flipping medical images: use randomized flipping, through which the image
information is mirrored horizontally or vertically.

4.2 Training phase
The dataset was split into training (80%) and testing (20%) sets; the 20% of the
training set is used as validation set, in order to monitor the training process
and prevent overfitting.
All experiments have been performed on a machine equipped with a 12 x86 64
Intel(R) Core(TM) CPUs @3.50GHz, running GNU/Linux Debian 7 and using
CUDA compilation tools, release 7.5, V 7.5.17 NVIDIA Corporation GM 204 on
GeForce GTX 970.
Fine-tuning For the training phase we performed hyperparameters opti-
mization. DenseNet 169 was trained with both optimizers Adam and SGD and
for each optimizer 7 learning rate were tried. The best performance is obtained
with the following configuration, trained for 300 epochs: Adam optimizer, learn-
ing rate 10−5 , batch size 16, and binary cross-entropy as loss function.
The configuration of networks was modified in terms of the number of nodes
or levels to optimize the performance. We empirically changed the number of
layers and we trimmed network size by pruning nodes to improve computational
performance and identify those nodes which would not noticeably affect network
performance. However, since we performed the experiments using well-know net-
works already optimized, we achieved the best performance using the standard
configuration as originally proposed by respective authors.
We performed 10-fold cross-validation in order to choose the parameter value
that gives the lowest cross-validation average error; experiments were performed
on the very same machine with the same configuration of the other approaches.

4.3 Performance Metrics
We assessed the effectiveness of our approach by measuring Area Under the
Curve (AUC) and Recall, especially focusing on the last one; indeed, in this
context, the most important thing is to minimize False Negatives (i.e., disease
is present but is not identified).
Let T P be a True Positive, T N a True Negative, F N a False Negative,
and F P a False Positive, a ROC curve is a plot of true positive fractions
(Se = T PT+FP TN
N ) versus false positive fractions (Sp = 1 − T N +F P ) by varying
the threshold on the probability map. Closer a curve approaches the top left
corner, then better is the performance of the system.
The Area Under the Curve (AUC), which is 1 for a perfect system, is a single
measure to quantify this behavior [12].
Recall (Rec = T PT+F P
N ) considers prediction accuracy among only actual
positives and explain how correct our prediction is among all people.

5 Results and Discussion
Table 2 and Table 3 report classification results after 10-fold cross-validation for
all datasets in terms of Recall and AUC, respectively. Even though promising
results are achieved in all DenseNet-based experiments, DenseNet 169 shows the
most efficient architecture: if reports AUC mean value of 0.95 and Recall mean
value of 0.90 over all the classes: hence, it was the one selected for the study.
The herein proposed approach achieves promisingly results; in particular,
DenseNet 169 achieves the best performance on COVID-19 dataset (i.e., Recall
mean value: 0.99 and AUC: 0.99), instead, it decreases on Viral Pneumonia
dataset (i.e., Recall mean value: 0.83 and AUC: 0.92), and in classifying Healthy
patients (i.e., Recall mean value of 0.80 and AUC: 0.89).
Table 2: Validation Recall (and standard deviation) for the 4 tested neural networks
after 10-fold cross-validation for each dataset. Most significant results hare highlighted.

DATASET DenseNet 121 DenseNet 169 DenseNet 201 Inception V3
0.98 0.99 0.96 0.94
COVID-19
(0.01) (0.00) (0.01) (0.01)
0.79 0.83 0.80 0.77
Viral Pneumonia
(0.07) (0.05) (0.08) (0.08)
0.95 0.96 0.90 0.88
Streptococcus Pneumonia
(0.01) (0.01) (0.01) (0.01)
0.78 0.80 0.82 0.84
Healthy patients
(0.04) (0.04) (0.03) (0.02)

Table 3: AUC values for the 4 tested neural networks after 10-fold cross-validation for
each dataset. Most significant results hare highlighted.

DATASET DenseNet 121 DenseNet 169 DenseNet 201 Inception V3
COVID-19 0.99 0.99 0.99 0.97
Viral Pneumonia 0.91 0.92 0.91 0.88
Streptococcus Pneumonia 0.97 0.98 0.97 0.94
Healthy patients 0.87 0.89 0.88 0.82

Analyzing the results, we see that Viral Pneumonia is often confused with
Streptococcus Pneumonia, due to overlapping imaging characteristics and thus
resulting in a Recall mean value always less than 0.90 in all experiments per-
formed. It is worth noting that, in general, the extraction of CT scan images from
published articles, rather than from actual sources, might lessen image quality,
thus affecting performance of the machine learning model.
A visual inspection of the GradCAM results confirms the quality of the
model; indeed, our model exhibits strong classification criteria in the Chest re-
gion (see Fig. 5). In particular, red areas refer to the parts where the attention
is strong, while blue areas refer to weaker attention. In general, the warmer the
color, the more important are the features highlighted for the network.
Moreover, in order to confirm that the identified portions are actually signifi-
cant, for each dataset we selected and removed the 40% of highlighted elements;
this threshold was selected empirically after several experiments. A substantial
decrease of Recall (on average around 10%) is shown using COVID-19, Viral
Pneumonia and Streptococcus Pneumonia (i.e., p-value < 0.05 for paired t-test
computed before and after images cutting); no statistical changes are shown
using dataset of Healthy patients. This result suggests that GradCAM is able
to identify the important elements involved in the training process and, conse-
quently, responsibility for this diminishment is due to images cutting by which
we removed the peculiar characteristic of the disease.
(a) COVID-19 (b) Viral Pneumonia

Fig. 5: Visual example of achieved results. For each diagnostic class, we show raw Chest
X-ray image (left) and GradCAM result (right). Images on the right sides highlight
the most important areas involved in the classification process.

6 Conclusion

In this work we exploit the use of CNNs and visual explanation techniques to
estimate diagnosis using Chest X-ray and to analyze the internal processes per-
formed by a neural network during the training phase with the aim of improving
explainability in the process of making qualified decisions. Basically, we try to
identify the most important regions that influence the network’s decisions.
We fine-tuned the approach by means of accurate experimental activities; in
particular, we classified four different disease datasets, and four different CNNs
for the classification.
Experimental results show that our proposal is robust and it is able to identify
specific regions that are crucial in the neural network decision-making process,
thus improving explainability. Indeed, classification accuracy is lower when high-
lighted regions are removed from the input images; this suggests the importance
of these areas in disease classification and the possibility to consider the set of
elements identified as potential disease markers.
In context where early and accurate medical diagnosis of specific pathologies
are essential, our method proves that visual explanation method combined with
machine learning techniques can be used to provide solid disease classifications
and automatically discover new bio-markers by interpreting network decisions.
As future work is concerned, we plan to investigate misclassification errors
and improve the generalization capability of the model. Our efforts will also
include the interaction with physicians, so that proper medical expertise can be
used to judge and better assess the quality of the regions highlighted by the
proposed approach.

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