Deep Learning Approaches for Detection of Covid-19 Using Chest X-Ray Images

Abstract

According to studies, covid-19 affects the respiratory tract along with the lungs. Thus it is critical to efficiently detect and diagnose COVID-19 and the non-COVID i.e., pneumonia/normal cases at the earliest. This helps in reducing the fast spread and stops the condition from becoming virulent. Deep learning models are found to have an extraordinary capacity in providing accurate results, forming an efficient system for detecting COVID-19 as well as pneumonia. Here, 6432 images of chest X-ray, consisting of 5144 images for training and 1288 images for testing, each containing 3 classes - Covid-19 affected, Pneumonia affected and normal were used. The data was preprocessed and a comparison of VGG-16, ResNet-50, and CNN models were done. Models classify data as Covid-19 or Pneumonia or Normal. In the final analysis, the ResNet-50 model gave the highest accuracy of 96.61 percentage followed by VGG-16 with an accuracy of 94.58 percentage, and CNN model with an accuracy of 88.98 percentage .