Article
An Effective Convolutional Neural Network Model for the
Early Detection of COVID-19 Using Chest X-ray Images
Muhammad Shoaib Farooq
1
, Attique Ur Rehman
2
, Muhammad Idrees
3
, Muhammad Ahsan Raza
4
,
Jehad Ali
5,
* , Mehedi Masud
6,
* , Jehad F. Al-Amri
7
and Syed Hasnain Raza Kazmi
1
Citation: Farooq, M.S.; Rehman,
A.U.; Idrees, M.; Raza, M.A.; Ali, J.;
Masud, M.; Al-Amri, J.F.; Kazmi,
S.H.R. An Effective Convolutional
Neural Network Model for the Early
Detection of COVID-19 Using Chest
X-ray Images. Appl. Sci. 2021, 11,
10301. https://doi.org/10.3390/
app112110301
Academic Editor: Keun Ho Ryu
Received: 3 September 2021
Accepted: 31 October 2021
Published: 2 November 2021
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4.0/).
1
Department of Computer Science, School of System and Technology, University of Management and
Technology, Lahore 54000, Pakistan; shoaib.farooq@umt.edu.pk (M.S.F.);
syedhusnainali11@gmail.com (S.H.R.K.)
2
Department of Computer Science, Lahore Garrison University, Lahore 54000, Pakistan;
f2019288002@umt.edu.pk
3
Department of Computer Science and Engineering, UET Lahore, Narowal Campus, Lahore 54890, Pakistan;
midrees10@uet.edu.pk
4
Department of Information Technology, Bahauddin Zakariya University, Multan 60000, Pakistan;
ahsan_0136@yahoo.com
5
Department of Computer Engineering, and Department of AI Convergence Network, Ajou University,
Suwon 16499, Korea
6
Department of Computer Science, College of Computers and Information Technology, Taif University,
P.O. Box 11099, Taif 21944, Saudi Arabia
7
Department of Information Technology, College of Computers and Information Technology, Taif University,
P.O. Box 11099, Taif 21944, Saudi Arabia; j.alamri@tu.edu.sa
* Correspondence: jehadali@ajou.ac.kr (J.A.); mmasud@tu.edu.sa (M.M.)
Abstract:
COVID-19 has been difficult to diagnose and treat at an early stage all over the world. The
numbers of patients showing symptoms for COVID-19 have caused medical facilities at hospitals
to become unavailable or overcrowded, which is a major challenge. Studies have recently allowed
us to determine that COVID-19 can be diagnosed with the aid of chest X-ray images. To combat
the COVID-19 outbreak, developing a deep learning (DL) based model for automated COVID-19
diagnosis on chest X-ray is beneficial. In this research, we have proposed a customized convolutional
neural network (CNN) model to detect COVID-19 from chest X-ray images. The model is based on
nine layers which uses a binary classification method to differentiate between COVID-19 and normal
chest X-rays. It provides COVID-19 detection early so the patients can be admitted in a timely fashion.
The proposed model was trained and tested on two publicly available datasets. Cross-dataset studies
are used to assess the robustness in a real-world context. Six hundred X-ray images were used for
training and two hundred X-rays were used for validation of the model. The X-ray images of the
dataset were preprocessed to improve the results and visualized for better analysis. The developed
algorithm reached 98% precision, recall and f1-score. The cross-dataset studies also demonstrate the
resilience of deep learning algorithms in a real-world context with 98.5 percent accuracy. Furthermore,
a comparison table was created which shows that our proposed model outperforms other relative
models in terms of accuracy. The quick and high-performance of our proposed DL-based customized
model identifies COVID-19 patients quickly, which is helpful in controlling the COVID-19 outbreak.
Keywords: convolutional; COVID-19; neural network; chest X-ray; model; detection
1. Introduction
A coronavirus known as the SARS-CoV-2 has caused a global pandemic as stated
by world health organization (WHO) [
1
]. It is a disease caused by an infection by a
newly discovered type of coronavirus [
2
]. This virus is part of a wider family of previously
discovered viruses that can cause infectious diseases in the respiratory system of the human
body such as coughs and colds [
3
]. It also causes severe illnesses, such as pneumonia,
Appl. Sci. 2021, 11, 10301. https://doi.org/10.3390/app112110301 https://www.mdpi.com/journal/applsci
