COVID‐19 Detection from Chest X‐Ray (CXR) Images Using Deep Learning Models

Abstract

Due to the tremendous rise in COVID cases around the world, early detection of Covid-19 has become critical. Deep learning technology has recently sparked a lot of attention as a means of detecting and classifying diseases quickly, automatically, and accurately. The goal of this study is to develop a deep learning based automatic COVID‐19 detection system for better, faster, and more accurate COVID‐19 detection from chest X‐Ray (CXR) images. In our work, we have used pre-trained deep learning models such as VGG16, ResNet50, DenseNet201, InceptionV3 and Xception utilizing openly accessible dataset. Experimental results show that the DenseNet201 model performs the best with more than 97% accuracy. Moreover, in terms of size, DenseNet121 is beating the rest of the models. As a results, DenseNet201 is most suitable Deep Convolutional neural networks (CNN) architecture for developing an automatic covid-19 detection tool.