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, Azlan Mohd Zain
2021 International Journal of Environmental Research and Public Health  
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
more » ... niques. 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.
doi:10.3390/ijerph181910147 pmid:34639450 fatcat:wc3vgctenzdlhl3rs5x4bsc73q