A Novel Convolutional Neural Network Architecture to Diagnose COVID-19

Abstract

The utilization of imaging information has been accounted to be helpful for quick diagnosis of COVID-19. Although X-ray gives an assortment of indications brought about by the viral disease, given a lot of images, these visual highlights are troublesome and can set aside a long effort to be perceived by radiologists. Computerized techniques for mechanized grouping of COVID-19 on X-ray have been discovered to be promising. This paper presents an examination of the Convolutional Neural Network for the classification of COVID-19 utilizing a huge public data set of Chest X-rays gathered from COVID-19 patients and non-COVID-19 subjects. The outcomes show that utilizing various epoch counts for various optimizers such as Adaptive Moment Estimation (Adam), Stochastic Gradient Descent (SGD), and Root Mean Square Prop (RMSProp), it was obvious that APST-Net with Adam optimizer attains the highest training, validation, and F1-score of 98.45%, 98.20%, and 98.18% respectively. As binary classification was carried out, Sigmoid was chosen as the classifier and Binary-Cross Entropy is used as the loss function. The proposed model APST-Net was compared with various pre-trained models and it was concluded that the APST-Net, which is the most profound architecture, is prodigious in terms of precision, balance among affectability and particularity, and F1 score, has accomplished superior classification results. The data augmentation and manual extraction of regions of interest on the Chest X-ray images are adopted by the current implementation for COVID-19 classification.