Comparison Between ResNet 16 and Inception V4 Network for COVID-19 Prediction

Abstract

COVID-19 claimed 5 million lives worldwide so far, and the count is continuing. It also affected socio-economic life of almost everybody in the world. Due to COVID-19, mortality and morbidity are continuing, and it is necessary to find new methods and techniques to contain the infection. Every government is trying hard to implement a new strategy to minimize the spread of the virus. COVID-19 infection occurs due to the virus strain SARS-COV-2. Generally, death occurs due to COVID-19 because of suppurative pulmonary infection and subsequent septic shock or multiorgan failure. In the literature, there are some computational techniques which use deep learning models and reported fairly good performance. This paper proposes a new deep learning architecture inception v4 to automatically detect COVID-19 using the chart X-ray images. The proposed methodology provided improved performance of 98.7 and 94.8% of training and validation accuracy. The developed technology can be used to detect COVID-19 with a high performance; the same may be deployed by the various governments in the detection and the management of COVID-19 in an efficient manner. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.