A Relative Analysis of Different CNN Based Models for COVID-19 Detection using CXR and CT Images

Abstract

COVID-19 is an extremely contagious disease that transmits from person to person by contaminated droplets or virus containing airborne particles. It causes severe damage to a patient’s lungs by forming patchy pulmonary lesions and consolidations, which are apparent in chest radiographs such as CXR (X-ray) and CT (computed tomography) images. Therefore, CXR and CT are considered crucial sources of information for early detection of COVID-19 infection. Manual inspection of this information requires expertise, high manpower and substantial amount of time. In order to tackle these issues, deep learning techniques can be utilized in the field of COVID-19 detection. This paper aims at analyzing the performance of different convolutional neural network models in COVID-19 detection using CXR and CT images. These models employ transfer learning and are formed by combining four well-known convolution bases with five distinct machine learning classifiers. All the models are comprehensively trained and tested on CXR and CT datasets each and are thoroughly compared with one another in terms of various evaluation metrics. Amongst these models, the best classification accuracy of 91.18% is provided by the Inception V3 with a neural network classifier on CXR images. Moreover, to assess the improvement of a COVID-19 detection method due to using different techniques, a comparative study of these transfer learning based models with other existing frameworks is also provided.