Detection of COVID-19 in Chest X-ray Images: A Big Data Enabled Deep Learning Approach

Mazhar Javed Awan, Muhammad Haseeb Bilal, Awais Yasin, Haitham Nobanee, Nabeel Sabir Khan and Azlan Mohd Zain
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Mazhar Javed Awan: Department of Software Engineering, University of Management and Technology, Lahore 54770, Pakistan
Muhammad Haseeb Bilal: Department of Software Engineering, University of Management and Technology, Lahore 54770, Pakistan
Awais Yasin: Department of Computer Engineering, National University of Technology, Islamabad 44000, Pakistan
Nabeel Sabir Khan: Department of Computer Science, University of Management and Technology, Lahore 54770, Pakistan
Azlan Mohd Zain: UTM Big Data Centre, School of Computing, Universiti Teknologi Malaysia, Skudai 81310, Malaysia

IJERPH, 2021, vol. 18, issue 19, 1-16

Abstract: Coronavirus disease (COVID-19) spreads from one person to another rapidly. A recently discovered coronavirus causes it. COVID-19 has proven to be challenging to detect and cure at an early stage all over the world. Patients showing symptoms of COVID-19 are resulting in hospitals becoming overcrowded, which is becoming a significant challenge. Deep learning’s contribution to big data medical research has been enormously beneficial, offering new avenues and possibilities for illness diagnosis techniques. To counteract the COVID-19 outbreak, researchers must create a classifier distinguishing between positive and negative corona-positive X-ray pictures. In this paper, the Apache Spark system has been utilized as an extensive data framework and applied a Deep Transfer Learning (DTL) method using Convolutional Neural Network (CNN) three architectures —InceptionV3, ResNet50, and VGG19—on COVID-19 chest X-ray images. The three models are evaluated in two classes, COVID-19 and normal X-ray images, with 100 percent accuracy. But in COVID/Normal/pneumonia, detection accuracy was 97 percent for the inceptionV3 model, 98.55 percent for the ResNet50 Model, and 98.55 percent for the VGG19 model, respectively.

Keywords: COVID-19; corona virus; pneumonia; chest X-ray; CNN; transfer learning; big data; public health; data bricks; Apache Spark; ResNet50; InceptionV3; VGG19; SparkDL; machine learning; deep learning (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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