Speed-enhanced convolutional neural networks for COVID-19 classification using X-rays

Abstract

COVID-19 emerged as a pandemic in December 2019. This virus targets the pulmonary systems of humans. Therefore, chest radiographic imaging is required to monitor effect of the virus, prevent the spread and decrease the mortality rate. Imaging-based testing leads to a high burden on the radiologist manually screening the images. To make the imaging-based method an efficient diagnosis tool, screening automation with minimum human interference is a necessity. It opens numerous challenges for scientists and researchers to develop automatic diagnostic tools for COVID-19 detection. In this paper, we present two speed-enhanced convolutional neural networks (SECNNs) to automatically detect COVID-19 among the X-rays of COVID-19, pneumonia and healthy subjects. For 2-class classification (2CC) and 3-class classification (3CC), we named the models SECNN-2CC and SECNN-3CC respectively. The scope of this work is to highlight the significance and potential of CNN models built from scratch in COVID-19 identification. We conduct six experiments using six different balanced and imbalanced kinds of datasets. In the datasets, All X-rays are from different patients therefore it was more challenging for us to design the models which extract abstract features from a highly variable dataset. Experimental results show that the proposed models exhibit exemplary performance. The highest accuracy for 2CC (COVID-19 vs Pneumonia) is 99.92%. For 3CC (COVID-19 vs Normal vs Pneumonia), the highest accuracy achieved is 99.51%. We believe that this study will be of great importance in diagnosing COVID-19 and also provide a deeper analysis to discriminate among pneumonia, COVID-19 patients and healthy subjects using X-rays.