Convolutional Neural Network based COVID-19 Recognition from X-Ray Image

To halt extreme spread of Coronavirus(COVID-19), proper detection is the need of the hour. The number of physicians is negligible to serve the immense number of COVID-19 affected patients. For this reason, it is essential to automate the detection system of COVID-19 disease. In this proposed work, a convolutional neural network (CNN) based COVID-19 diagnosis system is developed to automate COVID- 19 disease detection using images of chest X-rays. The proposed model can differentiate three varieties: COVID-19, pneumonia and normal(healthy) from the X-ray images. Experimental process has been performed upon two publicly available datasets: COVID-19 Radiography Database and COVID-5K. Five deep convolutional neural network architectures: Xception, ResNet-50, Inception-v1, Inception-v2, and Inception-v3 are discretely used to train the system. The evaluation of the proposed system proves that Xception has provided the best performance with 99.47% accuracy, 99.21% sensitivity, 99.60% specificity, and 99.21% F1- score. The resultant of the experiment illustrates an improvement in the performance compared to some existing research works.