Automated Model for Tracking COVID-19 Infected Cases till Final

Diagnosis

Mohamed A. Gomaa

1 a

, Mustafa Wassel

1 b

, Rouzan M. Abdelmawla

1 c

, Nihal Ibrahim

1 d

Khaled Nasser

1 e

, Nermin A. Osman

2 f

and Walid Gomaa

1,3,∗ g,∗

Faculty of Engineering, Alexandria University, Egypt

Biomedical Informatics and Medical Statistics Department, Medical Research Institute, Alexandria University, Egypt

Department of Computer Science and Engineering, Egypt Japan University of Science and Technology, Alexandria, Egypt

Keywords:

Corona Virus Disease 2019, Pneumonia, COVID-19, Deep Learning, Convolution Neural Network (CNN),

Chest Radiology, X-ray, CT-Scan, Medical Imaging, Polymerase Chain Reaction (PCR).

Abstract:

The COVID-19 pandemic is now devastating. It affects public safety and well-being. A crucial step in the

COVID-19 battle will be tracking the positive cases with convenient accuracy of diagnosis. However, the

time of pandemics shows the emergent need for automated diagnosis to support medical staff decisions in

different steps of diagnosis and prognosis of target disease like medical imaging through X-rays, CT-Scans, etc.

Besides laboratory investigation steps, we propose a system that provides an automated multi-stage decision

system supported with decision causes using deep learning techniques for tracking cases of a target disease

(COVID-19 in our paper). Encouraged by the open-source Data sets for COVID-19 infected patients’ chest

radiology, we proposed a system of three Consecutive stages. Each stage consists of a deep learning binary

classiﬁer tailored for the detection of a speciﬁc COVID-19 infection feature from chest radiology, either X-ray

or CT-scan. By integrating the three classiﬁers, a multi-stage diagnostic system was attained that achieves an

accuracy of (87.980 %), (78.717%), and (84%) for the three stages, respectively. By no means a production-

ready solution, our system will help in reducing errors caused by human decisions, taken under pressure, and

exhausting routines, and it will be reliable to take urgent decisions once the model performance achieves the

needed accuracy.

1 INTRODUCTION

Active monitoring of affected patients is a crucial

phase in the battle against COVID-19, ensuring that

any exposed patient will seek prompt diagnosis and

care, as well as be isolated to reduce the spread of the

contagious Virus. The primary screening tool used

to identify COVID-19 cases is the polymerase chain

reaction (PCR) test, which can diagnose COVID-19

RNA from respiratory specimens (Hammoudi et al.,

https://orcid.org/0000-0001-7594-1137

https://orcid.org/0000-0002-2048-7624

https://orcid.org/0000-0001-6328-132X

https://orcid.org/0000-0002-5246-2106

https://orcid.org/0000-0001-8886-7044

https://orcid.org/0000-0001-7845-1854

https://orcid.org/0000-0002-8518-8908

∗

All authors contributed equally

2020). Although PCR testing is the gold standard

because it is highly reactive, it is a time-consuming,

laborious, and complicated manual method that is in

short supply (Wang and Wong, 2020).

An alternative screening method that has also been

used for COVID-19 screening has been the radiog-

raphy test in which chest X-ray imaging, e.g., X-ray

or computed tomography (CT) imaging, is performed

and analyzed by radiologists for visual indicators as-

sociated with COVID-19 viral infection. Early stud-

ies have shown that patients reveal malformations in

chest radiography that are characteristic of those in-

fected with COVID-19, with some suggesting that the

X-ray examination could be used as a primary tool for

the screening of COVID-19 in epidemic areas (Wang

and Wong, 2020),(Acharya and Satapathy, 2020) and

(Xu et al., 2020).

With the massive increase in the number of infec-

tions and suspect patients, it is arduous to perform

Gomaa, M., Wassel, M., Abdelmawla, R., Ibrahim, N., Nasser, K., Osman, N. and Gomaa, W.

Automated Model for Tracking COVID-19 Infected Cases till Final Diagnosis.

DOI: 10.5220/0010237401430154

In Proceedings of the 14th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2021) - Volume 5: HEALTHINF, pages 143-154

ISBN: 978-989-758-490-9

143

polymerase chain reaction (PCR) testing on all these

people. Notably, the prevalence of inﬂuenza becomes

signiﬁcantly greater amid the active ﬂu season (Ham-

moudi et al., 2020) and (Xu et al., 2020).

Empowered by the need for better processing of

radiography, a variety of deep-learning techniques

have been developed, and tests have shown to be very

encouraging in terms of precision in the identiﬁca-

tion of COVID-19-infected patients (Xu et al., 2020),

(Wang et al., 2020) and (Hammoudi et al., 2020).

In this paper, we propose an Automated Model for

Tracking COVID- 19 Infected Cases till Final Diag-

nosis, which is a pipeline for automatic detection of

pneumonia from chest radiography images using the

Convolution deep neural network

. We use different

data sets to train and validate the system pipeline to-

wards the automatic differentiation between various

pneumonia diseases and the novel COVID-19 virus.

The purpose of the model pipeline is developing an

assisting protocol for the medical staff that they can

use to decide if the suspect patient needs to do the

COVID-19 testing or other treatment protocol. The

remarkable added value of our system is the use of

CNN to fasten the process of chest radiology analy-

sis.

The paper is structured according to this. Firstly,

Section 2 provides a brief description of the contri-

butions made by other researchers in this area. Sec-

tion 3 addresses the methods used to construct the

proposed automated model for tracking COVID- 19

infected cases up until the ﬁnal diagnosis, the sys-

tem design and phases of the pipeline, the layout of

the architecture for each stage of the system pipeline,

and the data-set used per step. Section 4 describes the

speciﬁcs of deployment, training parameters for sys-

tem stages, and a description of the results obtained

from the technique of Class Activation Map (CAM).

Section 5 describes and examines the ﬁndings of tests

performed to determine the feasibility of the planned

pipeline. Finally, conclusions are seen, and further

directions are explored in Section 6 and Section 7.

2 RELATED WORK

Image processing and machine learning methods of-

ten have broad precision health applications. Various

COVID-19 based research is increasingly being per-

formed to illustrate some of the principles and actual

evidence regarding this epidemic. Many image classi-

ﬁcation, assessment, and decision-making techniques

relating to COVID-19 and radiography examinations

Codes and Models are available upon request

are outlined below.

In (Khalifa et al., 2020), a prediction of x-ray

pneumonia chest dependent on generative adversar-

ial networks (GAN) was introduced, with a ﬁne-tuned

deep transfer learning for a small data collection. Us-

ing GAN positively improves the proposed robustness

of the system and makes it resistant to the issue of

overﬁtting, which also helps to produce more images

from the data collection.

In (Mahmud et al., 2020), a novel architecture

of profound neural networks is suggested based on

depth-wise dilated convolutions. For the initial train-

ing level, large databases comprising non-COVID

pneumonia X-rays are utilized that are easily adapted

to use smaller COVID-19 X-rays databases. The

suggested stacking algorithm mutually converges fea-

tures generated from various X-ray resolutions. The

medical analysis is conducted by evaluating the acti-

vation model depending on the gradients.

In (Farag et al., 2020), an end-to-end parallelized

learning model has been developed that is capable

of taking advantage of multiple X-ray data sets of

Pneumonia-like infections in a single neural archi-

tecture, executing three tasks simultaneously; iden-

tiﬁcation, segmentation, and localization. The MTL

general encoder and the classiﬁcation algorithm head

are pre-trained on the standardized data set to be

allowed to identify 14 viral infections COVID-19

among them.

In (Rahman et al., 2020), an automatic diagnosis

of bacterial and viral pneumonia using an x-ray vi-

sion model has been developed. It includes a compre-

hensive update on the gains achieved in the success-

ful diagnosis of pneumonia. Four separate deep Con-

volutional Neural Network (CNN) pre-trained models

were tested. In this review, the authors recorded three

classiﬁcation schemes: normal vs. pneumonia, bac-

terial vs. viral pneumonia, and normal, bacterial, and

viral pneumonia.

In (Fang et al., 2020), the objective of this study

was to equate the response of chest CT with those of

viral nucleic acid assay at the preliminary patient di-

agnosis. The ﬁndings endorsed the need for chest CT

for COVID-19 screening in patients with the clinical

and epidemiological highest correlation with COVID-

19 infection, particularly once the results of PCR tests

are negative.

In (Yang et al., 2020), a data collection of COVID-

19 CT containing 349 healthy COVID-19 CT images

from 216 patients was obtained. Besides, the utility

of this collected data has been checked for the devel-

opment of COVID-19 diagnostic models by labora-

tory studies. Also, an approach focused on multi-task

learning as well as comparison self-supervised learn-

HEALTHINF 2021 - 14th International Conference on Health Informatics

144

ing has been developed to increase diagnostic perfor-

mance to a clinically meaningful degree.

In (Li and Xia, 2020), the research was pro-

posed to assess the risk of misdiagnosis of coron-

avirus (COVID-19) radiologists and to examine the

efﬁciency of chest CT in the diagnosis and monitor-

ing of COVID-19. The CT features of COVID-19 are

documented and compared to the CT features of other

viruses in order to familiarise radiologists with poten-

tial CT trends.

3 METHODOLOGY

AI technologies have been promoting remote opera-

tions and helping to deal with the shortage of qualiﬁed

radiologists. With the rapid development of computer

technology, digital image processing technology has

been widely used in the medical ﬁeld, including or-

gan segmentation and image enhancement and repair,

providing support for subsequent medical diagnosis.

Deep learning technologies, such as the Convolution

Neural Network (CNN) with a strong capacity for

nonlinear modeling, often have extensive applications

in medical image processing.

At this point, several AI-based devices and X-ray

image databases are private resources. Deep learning

performance and accuracy increase with the increase

of the amount of the user data to train the model [Fig-

ure 1]. The intended Automated Model for tracking

COVID- 19 infected cases till the ﬁnal diagnosis is

composed of three main consequent steps where the

pipeline of the system was designed and inspired by

the open-source COVID-19 Chest X-ray and CT data-

sets.

Figure 1: Deep Neural Network Data and Performance

trade Off.

The CT-scanners emit X-rays. Various tissue

types absorb X-rays in different proportions, and the

resultant contrasts offer accurate representations of

anatomy and disease. Absorbed radiation can sever

chemical bonds in tissues that will destroy DNA and

cause cancer because the cells are unable to rebuild

themselves (Schmidt, 2012).

That is why respiratory infections can be more

immediately apparent in CT images than in X-ray

images of the chest. However, the identiﬁcation of

COVID-19 from chest X-ray images is most often

studied as they reﬂect generic tools that are frequently

examined, unlike CT-scans.

The main reason for using X-ray along with CT-

scans images, especially in early-stage diagnostics,

backs to the fact of the high risk and cost of CT-scans

images. Therefore, Automatic detection must under-

take a range of identiﬁcation and classiﬁcation pro-

cedures to differentiate between COVID-19 and other

viral or bacterial infections.

3.1 Pipeline

The model is composed of several steps of classiﬁca-

tion. It mimics human performance with an additive

higher process speed. As mentioned earlier, it acts as

a helping hand to the medical staff, so it uses the same

steps used by humans. The system pipeline is shown

in [Figure 2]. It is divided into three stages. Firstly,

the radiography is classiﬁed for either having a Pneu-

monia or being normal having Pneumonia. It will go

to the next classiﬁer to decide if this infection is bac-

terial or viral. If it were bacterial, this patient would

need to subject to suitable treatment. If not bacteria,

he will use the third classiﬁer to ﬁnd out if it COVID-

19 or other viral infections. The result of the third

classiﬁer will nominate the patient whether to subject

to the PCR testing or rest for 14 days.

3.2 First Stage

COVID-19 is a respiratory disorder. The virus will

move across the lung tissue of the individual, causing

inﬂammation. According to the World Health Organ-

isation (WHO), extreme pneumonia is the most fre-

quent condition of moderate COVID-19, which is a

dangerous lung inﬂammation (Diaz et al., 2020). It

may be fatal to certain people, particularly the elderly

and those with respiratory disorders. Respectively, an

efﬁcient classiﬁer was developed to automatically de-

tect if a query chest X-ray image is Normal or Pneu-

monia.

3.2.1 Architecture Design

In this stage, we construct the initial network design

prototype [Figure 3] to make one of the following

Automated Model for Tracking COVID-19 Infected Cases till Final Diagnosis

145

Figure 2: Automated Tracking System pipeline.

two predictions a) Normal, b) Pneumonia. The ra-

tionale for choosing this network design is that it can

aid clinicians in deciding who should be prioritized

for second-stage testing for COVID-19 case conﬁr-

mation.

It can be observed that the First stage Classiﬁer

network architecture makes heavy use of Spatial Sep-

arable Convolutions, convolutions that can be sepa-

rated across their spatial axes, thus yield the same re-

sult with fewer multiplications, and hence you require

fewer computational resources. Thus, it will perform

classiﬁcation in the early stage with the simplest yet

efﬁcient network for Pneumonia and normal cases.

3.2.2 Data Features

The use of X-ray is due to the easiness of detecting

pulmonary symptoms from X-ray despite its cause.

The network acts as the radiologist looking for white

spots in the lungs (called inﬁltrates) that identify an

infection [Figure 4b]. This test will also help deter-

mine if you have any complications related to pneu-

monia such as abscesses or pleural effusions (ﬂuid

surrounding the lungs) [Figure 4] (Rajpurkar et al.,

2017).

3.2.3 Data Set Sources

For our experiment’s ﬁrst stage, we exploited

Chest X-ray images from covid-chestxray-data-

set at: https://www.kaggle.com/praveengovi/

coronahack-chest-xraydataset. This data set is

related to Automated methods to detect and classify

human diseases from medical images. Novel Ma-

chine Learning Algorithms and neural networks help

reduce the Corona Virus detection time and aids the

physicians to drive the consultation in better ways.

This data set contains 5,910 unique X-ray images

collected from public sources as well as through

indirect collection from hospitals and physicians.

3.2.4 Data Augmentation

The practice of data augmentation is an effective way

to increase the size of the training set. Augmenting

the training examples allow the network to ‘see’ more

diversiﬁed, but still representative, data points during

training.

Due to the unbalanced nature of the given classes

and the small size of our data set, also to improve our

classiﬁer quality to escape away from overﬁtting, we

have created our part of data based on the real data

set using data augmentation. We extend the original

data set by taking an image from the original data set

and apply some random functions to get random ef-

fects on each image. We have utilized the following

techniques: ﬂipping, rotating, brightness change, and

zooming. These techniques can be used to get the de-

sired amount to balance classes.

Note: by applying this augmentation, we make

sure to create a small portion of data to the other

class to make the classiﬁer get used to this augmen-

tation data. The augmentation must be created for all

classes, not just the unbalanced classes but not with

the same percentage.

The original data set has the distribution as shown

in [Figure 5a], the ratio of two classes was 1 : 2.75

normal to pneumonia images, after augmentation, we

have to work to decrease the large difference between

them [Figure 5b] as the results were 1 : 1.5 normal to

pneumonia.

3.2.5 Data Set Distribution

The data set is composed of 5,910 unique X-ray im-

ages divided into two categories, namely Normal and

Pneumonia. The Pneumonia images contribute 73%

of the data set and normal images represent 27%. The

data was spilled into a train and test sets with ration

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Figure 3: First stage classiﬁer, sequential long-range connectivity can be observed as it is dedicated to pulmonary symptoms

detection from chest radiography images. The heavy use of the Spatial Separable Convolutions in the network architecture is

observed, which makes strong balance between computational efﬁciency and representation capacity.

(a) Normal Non-infected Lung.

(b) Pneumonia infected Lung.

Figure 4: Example chest radiography images of: (a) Normal

Lung, and (b) Pneumonia infected Lung were white spots in

the lungs (called inﬁltrates) that identify an infection appear.

90% training to 10% testing sets. The full data set dis-

tribution after augmentation indicated is in[Table 1].

3.3 Second Stage

Having detected Pneumonia infection, the next step

is to make sure the cause of the inﬂammation as

COVID-19 may share symptoms with other Bacterial

(a) Data set before augmentation.

(b) Data set after augmentation.

Figure 5: Example of covid-chestxray-data-set: (a) Data set

before augmentation, and (b) Data set after augmentation

where the number of normal class samples increased appar-

ently.

and viral infections. Generally speaking, The WHO

estimate that around 1.4 millions of children lost their

life due to the failure of detecting this lung disease at

its early stage (Acharya and Satapathy, 2020). Addi-

tionally, in the case of the COVID-19 pandemic, the

early diagnostics of viral infection play a major role

in reducing contagious spread.

Automated Model for Tracking COVID-19 Infected Cases till Final Diagnosis

147

Table 1: Data distribution of ﬁrst stage.

Total Normal Pneumonia

5,910 1,576 4,334

Train 5,286 1,342 3,944

Test 624 234 390

Viral pneumonia and bacterial pneumonia are the

two types that can cause severe damages to the human

respiratory system (Hammoudi et al., 2020). Differ-

ent types of clinical management are required for the

cure of these infections. Antibiotics are used to re-

cover the bacterial infected pneumonia while viral in-

fected patients need different medication and support-

ive care for the recovery of the disease. Therefore, we

propose an accurate automated deep learning-based

method to identify the different types of pneumonia

diseases (viral/bacterial) with X-ray imaging.

3.3.1 Architecture Design

Having proved its effectiveness in the early stage, we

used the same network architecture used before [Fig-

ure 3]. The features to be detected in the second clas-

siﬁcation of the pipeline are similar to that of the ﬁrst

classiﬁcation step, which grantee the same architec-

ture works well for the next task as well.

The fewer computational resources and small

model parameters discussed earlier in the paper make

it an appropriate choice for this step of classiﬁcation

for easily distinguishing viral pneumonia and bacte-

rial pneumonia.

3.3.2 Data Features

In the proposed stage, our main intention after identi-

fying pneumonia is to classify into its particular type

that is either viral pneumonia or bacterial pneumo-

nia. In the radiography image, in bacterial pneumonia

[Figure 6a], the alveoli become ﬁlled with the secre-

tion of the white inﬂammatory ﬂuid while in the vi-

ral pneumonia [Figure 6b], the chest is infected with

the white spots (Acharya and Satapathy, 2020). Viral

and bacterial pneumonia infections are distinguished

by analyzing the amount of white substance that is

spread across the chest X-ray image.

3.3.3 Data Set Sources

We have used the same Kaggle data set used in the

early stage classiﬁer for the training and testing of

the diseases. we exploited Chest X-ray images from

covid-chestxray-data-set at: https://www.kaggle.com/

praveengovi/coronahack-chest-xraydataset. Upon in-

vestigating the data set, it was decided to use it once

more for the second stage classiﬁer, as the data-set

(a) Bacterial pneumonia infection.

(b) Viral Pneumonia infection.

Figure 6: Example of chest radiography images of: (a) viral

Pneumonia infected Lung ﬁlled with secretion of the white

inﬂammatory ﬂuid , and (b) viral Pneumonia infected Lung

with white spots.

was already labeled with the particular type of Pneu-

monia (bacterial/viral). That is, due to its compatibil-

ity with the second stage classiﬁer, which is used for

labeling pneumonia infection into two classes; bacte-

rial pneumonia and viral pneumonia.

3.3.4 Data Set Distribution

The data set is composed of 4, 334 X-ray pneumo-

nia images categorized previously. It is divided into

two classes: bacterial pneumonia and viral pneumo-

nia. The data-set has 47% of bacterial pneumonia im-

ages to 53% of viral pneumonia. It is split into 90%

training and 10% testing sets. The full data set distri-

bution is indicated in [Table 2].

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Table 2: Data distribution of second stage.

Total viral Bacterial

4,334 1,555 2,777

Train 3,942 1,407 2,535

Test 390 148 242

3.4 Third Stage

The reason for the usage of the CT-scan images in

this advanced step of the process despite the risks dis-

cussed before is that if a suspect patient reached this

step, then there is a severe need for a high accuracy

scanning method to decide the type of viral infection.

That is why the third step of the pipeline depends

on the high accuracy of CT-scan images in detecting

COVID-19.

3.4.1 Architecture Design

Exposure to publicly accessible COVID-19 related

lung CT databases for deep learning studies is quite

restricted. Few open-access X-ray image collections

of the chest are freely available. To minimize the

training data gap, although the supply of COVID-19

open-source CT-scan imagery is limited, we turn to

learn transfer. Transfer Learning is a Machine Learn-

ing technique whereby a model is trained and devel-

oped for one task, then is re-used on a second related

task. Transfer Learning is usually applied when there

is a new data-set smaller than the original data-set

used to train the pre-trained model [Figure 7](Mahbub

et al., 2018).

Figure 7: Illustration of who transfer Learning might im-

prove the learning performance. (Figure reproduced and

adapted from (Tatiana et al., 2013))

Several pre-trained models were tested until ade-

quate results were obtained. Inception-v3 is a convo-

lutional neural network that is 48 layers deep. You

can load a pre-trained version of the model trained

on more than a million images from the ImageNet

database. Inception-v3 consists of two parts; Feature

extraction part with a convolutional neural network.

Classiﬁcation part with fully-connected and softmax

layers (Szegedy et al., 2016). VGG-19 is a convo-

lutional neural network that is 19 layers deep. You

can load a pre-trained version of the model trained

on more than a million images from the ImageNet

database. VGG19 is a variant of the VGG model,

which in short consists of 19 layers (16 convolution

layers, 3 fully connected layers, 5 MaxPool layers,

and 1 SoftMax layer). We can understand VGG as

a successor of the AlexNet (Simonyan and Zisser-

man, 2015). DenseNet-201 is a convolutional neu-

ral network that is 201 layers deep. You can load

a pre-trained version of the model trained on more

than a million images from the ImageNet database.

The DenseNets needs fewer parameters than a con-

ventional CNN counterpart because redundant func-

tion maps need not be taught (Huang et al., 2017).

COVID-Net is a deep convolutional layer neural net-

work architecture designed to detect COVID-19 cases

from chest CT-scan images, which are open source

and usable to the general public. It was trained on

chest x-ray data collection utilized to train COVID-

Net, which we refer to as COVIDx and consists of

16,756 chest x-ray images from two open-access data

in 13,645 patient cases (Wang and Wong, 2020).

3.4.2 Data Features

The CT-scan imaging of COVID-19 [Figure 8b],

presents several distinct manifestations according to

previous studies. The symptoms include focused

ground glass shadows primarily scattered in bilateral

lungs, numerous consolidation shadows followed by

a ’halo symbol’ of adjacent ground glass shadows in

both lungs, mesh shadows, inﬂating signals within the

lesions, numerous consolidations in varying sizes and

grid-shaped high-density shadows (Bernheim et al.,

2020).

3.4.3 Data Set Sources

CT scans are promising in providing accurate, fast

screening, and testing of COVID-19. There have been

several works studying the effectiveness of CT scans

in screening and testing COVID-19, and the results

are promising. However, owing to questions regard-

ing privacy. The CT scans used in such works are not

published (Yang et al., 2020).

To address this issue, we built our CT-COVID19-

data-set from several resources. We ﬁrst col-

lected only COVID-19 labeled images from covid-

chestxray-dataset at: https://github.com/ieee8023/

covid-chestxray-dataset which is an open data-set of

chest X-ray and CT images of patients which are pos-

itive or suspected of COVID-19 or other viral and

bacterial pneumonia. Second, we used COVID-19

and non COVID-19 images from COVID-CT data-

set at: https://github.com/UCSD-AI4H/COVID-CT

Automated Model for Tracking COVID-19 Infected Cases till Final Diagnosis

149

(a) Non-COVID-19 infection.

(b) COVID-19 infection.

Figure 8: Example of chest CT-scan radiography images

of: (a) Non-COVID-19 infection viral Pneumonia infection,

and (b) COVID-19 viral Pneumonia infection with focused

ground glass shadows primarily scattered in bilateral lungs.

which contain CT-scans positive for COVID-19 and

is open-sourced to the public. It was conﬁrmed by a

senior radiologist in Tongji Hospital, Wuhan, China,

who has performed diagnosis and treatment of a large

number of COVID-19 patients during the outbreak of

this disease between January and April. Finally, we

used Conﬁrmed cases CT-scans from kaggle COVID-

19 CT scans data-set at: https://www.kaggle.com/

andrewmvd/covid19-ct-scans which is a data-set con-

taining 20 CT scans as well as segmentation of lungs

and infections made by experts. This data-set required

prepossessing as the 20 CT scans exist in form of sev-

eral layers. We prepossessed it to ﬂatten the layers

and use the layers that only show infection.

3.4.4 Data Set Distribution

Our CT-COVID19-dataset composed of 1,255 CT-

scan images. It contains images from the three

used data-sets mentioned previously. From the ﬁrst

data set, we used one non-COVID-19 image and 22

COVID-19. For the second data set, We took 389 non-

COVID-19 images and 349 COVID-19. Concern-

ing the third data set, which contains only COVID-

19 images after the pre-processing, it contributes 494

COVID-19 images.

The distribution of the ﬁnal 1,255 image data-set

is 63.24% of COVID-19 images and 36.76% non-

COVID-19 images. It was split into training and test-

ing sets with the ratio 90% to 10%. The full data-set

distribution is indicated in [Table 3].

Table 3: Data distribution of third stage.

Total Non COVID COVID

1,255 390 865

Train 1,130 348 782

Test 125 42 83

4 IMPLEMENTATION DETAILS

Due to the mission-critical nature of clinical applica-

tions stating the experiment timing is crucial in the

evaluation of the current state. The experiments were

conducted during the period between May 2020 and

September 2020. The different used data-sets were

obtained, manipulated, and downloaded during the

pandemic outbreak between January 2020 and April

2020.

The proposed pipeline was constructed and tested

using the TensorFlow Backend Keras Deep Learn-

ing Software. The three pipeline stages were trained

and tested with differently tuned hyperparameters un-

til sufﬁcient results were reached. We use the hyper-

parameters in [Table 4] for training along with using a

learning rate policy where the learning rate decreases

when learning stagnates for a period of time.

Table 4: Training hyperparameters.

Stage 1

optimizer adam adam adam

learning rate 2e-5 2e-5 1e-5

number of epochs 50 100 100

batch size 64 64 32

factor 0.3 0.3 0.1

patience 2 2 3

4.1 Class Activation Map (CAM)

We propose a simple technique to expose the implicit

attention of Convolutional Neural Networks on the

image. It highlights the most informative image re-

gions relevant to the predicted class. This technique

will change the output of not only the image. Now

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150

we have a heat map [Figure 9b] indicating the regions

which have a considerable impact on the classiﬁcation

decision making .

The procedure of applying this technique is to di-

vide into two steps, ﬁrst save the weights of the last

convolution layer, the layer just before the dense lay-

ers, and add average pooling to this layer, second use

the saved weights to visualize this impact on the input

image.

(a) Original Pneumonia Infection.

(b) Pneumonia infection with CAM.

Figure 9: Example chest radiography images of: (a) original

Pneumonia infected , and (b) Pneumonia infected with the

effect of CAM.

5 EXPERIMENTAL RESULTS

The confusion matrix is used towards measuring the

prediction correctness. Prediction correctness of the

algorithm is expressed in this 2D array, which repre-

sents a list of numbers that reports the number of false

positives, false negatives, true positives, and true neg-

atives. These values are deﬁned as the following:

• True positives (TP): These are cases in which we

predicted yes (they have the disease), and they do

have the disease.

• True negatives (TN): We predicted no, and they

don’t have the disease.

• False positives (FP): We predicted yes, but they

don’t actually have the disease.

• False negatives (FN): We predicted no, but they

actually do have the disease.

Accuracy is the most common and easy metric to

use when measuring the output of a model. The ac-

curacy of a method determines how correct the val-

ues are predicted. The precision indicates the repro-

ducibility of the measurement or how many of the pre-

dictions are correct. Recall shows how many of the

accurate results are discovered. F

-score uses a com-

bination of precision and recall to calculate a balanced

average.

These metrics are often computed from a confu-

sion matrix for a binary classiﬁer. We can redeﬁne

accuracy as:

Accuracy =

T P + T N

T P + T N + FP + FN

(1)

Then we can check out precision as:

Precision =

T P

T P + FP

(2)

Also we can check out recall as:

Recall =

T P

T P + FN

(3)

Finally, We can check out F

-score as:

− score = 2

Precision.Recall

Precision + Recall

(4)

One more tool used to describe system perfor-

mance is the receiver operating characteristic (ROC)

curve. It is created by plotting the True Positive

Rate against False Positive Rate when you adjust

the threshold for granting observations to a particular

class.

[Table 5] presents the performance for classiﬁca-

tion of normal and pneumonia cases by using the pro-

totype architecture shown in [Figure 3]. It has reached

Automated Model for Tracking COVID-19 Infected Cases till Final Diagnosis

151

best performance with an average classiﬁcation accu-

racy of 87.98%. The Classiﬁcation test metrics are

presented in [Table 6]. Additionally, [Figure 10a]

Shows the ROC. The ROC curve of the ﬁrst stage with

threshold=0.5.

Table 5: First classiﬁer confusion matrix.

Actual

Pneumonia Normal

Predicted

Pneumonia 329 14

Normal 61 220

Table 6: First classiﬁer test metrics.

Accuracy Precision Recall F1-score

87.980% 95.918 % 84.359% 89.768

Similarly, [Table 7] presents performance for clas-

siﬁcation of bacterial pneumonia and viral pneumo-

nia cases with classiﬁcation accuracy of 78.717% and

with test metrics shown in [Table 8]. Additionally,

[Figure 10b] Shows the ROC curve of second Stage

with threshold=0.5.

Table 7: Second classiﬁer confusion matrix.

Actual

Viral Bacterial

Predicted

Viral 68 3

Bacterial 80 239

Table 8: Second classiﬁer test metrics.

Accuracy Precision Recall F1-score

78.717% 95.774 % 45.946% 62.100

Next, the VGG-19 model showed High sensitiv-

ity to CT-scan images of the COVID-19 test set since

it detects COVID-19 infection with accuracy of 84%.

[Table 9] shows a particularly robust pneumonia de-

tection of COVID-infected patients and satisfying test

metrics shown in [Table 10] in third stage test metrics.

Table 9: Third classiﬁer confusion matrix.

Actual

COVID

Non-

COVID

Predicted

COVID 40 18

Non-

COVID

2 65

Table 10: Third classiﬁer test metrics.

Accuracy Precision Recall F1-score

84% 97% 78% 87

Finally, we now take a profound exploration into

the results of test metrics. It can be observed that the

classiﬁers can achieve sufﬁcient accuracy and good

Precision, which is indicated in the very few false pos-

itive detections. Taking into consideration that many

false positives will raise the pressure on the health-

care system due to the need for extra PCR tests and

additional treatment.

6 CONCLUSIONS

In this research work, we have demonstrated a novel

approach to identify and classify the various types

of pneumonia disease. We have used different data-

sets and speciﬁc features that are relevant to the in-

fection. Compared to our intention, we have trained

our pipeline models with detecting speciﬁc features

at each stage, which makes the intended automated

Model for Tracking COVID- 19 Infected Cases till

Final Diagnosis a robust aiding tool for the medical

stuff. The system will lead to accelerating the devel-

opment of highly accurate yet practical deep learning-

based solutions for detecting COVID-19 cases from

chest radiography images that will help accelerate de-

ciding the suitable treatment protocol and limit the

contagious spread.

7 FUTURE WORKS

Future studies should improve the precision of the

differentiation between COVID-19 viral and non-

COVID-19 viral pneumonia when adequate amounts

of COVID-19 chest CT-scan images are accessible,

which would enable the precise detection of infected

patients with COVID-19, even in a non-epidemic set-

ting. Also, the robustness of the existing framework

must be validated by PCR and clinical tests ﬁeld train-

ing. However, our method has given added opportuni-

ties for new clinicians to identify the particular form

of pneumonia at an early level.

ACKNOWLEDGEMENTS

We would like to thank all of those who contributed

to this work with their helpful discussion and advice.

Special Thanks To Dr.Nermin Nabile from the fac-

ulty of Medicine Alexandria University; for her med-

ical aspects advice as well, Dr. Rania ElSharkawy;

Vice President of Alexandria University for Commu-

nity Service and Environment; for her helping during

system pipeline design also, for her continuous sup-

port.

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(a) ROC For ﬁrst stage. (b) ROC For Second stage.

Figure 10: ROC curves representing the performance of the proposed models (a) Normal vs Pneumonia (b) Bacterial Vs Viral

Pneumonia.

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