Automated COVID-19 screening framework via Deep Convolutional Neural Network with Chest X-ray Medical Images

Abstract

COVID-19 screening using chest X-rays plays a significant role in the early diagnosis of COVID-19 illness during the ongoing pandemic. Manually identifying this infection from chest X-ray films is a challenging and time-consuming technique due to time restrictions and the competence of radiologists. Also, the manual Covid-19 identification technique is made much more difficult and opaquer by the feature similarity between positive and negative chest X-ray images. Therefore, we propose an automated COVID-19 screening framework that utilizes artificial intelligence techniques with a transfer learning approach for COVID-19 diagnosis using chest X-ray images. Specifically, we employ the transfer learning concept for feature extraction before further processing with modified deep neural networks. Also, Grad-CAM visualization is used for our case study to support the predicted diagnosis. The results of the experiments on the publicly accessible dataset show that the convolutional neural network model, which is simple yet effective, performs significantly better than other deep learning techniques across all metrics, including accuracy, precision, recall, and F-measure.