An ensemble deep transfer-learning approach to identify COVID-19 cases from chest X-ray images

Abstract

Novel coronavirus began in Wuhan, China back in December 2019. It has now outspread all over the world. Around 23 million people are currently affected by the novel coronavirus. It causes around 800,000 deaths globally. There are just about 300,000 people contaminated by COVID-19 in Bangladesh too. As it is an exceptional new pandemic infection, its diagnosis is challenging for the medical community. In regular cases, it is hard for developing countries to test cases frequently. The RT - PCR test is a generally utilized analysis framework for COVID-19 case detection. However, by utilizing X-ray image-based programs, recognition can diminish the expense and testing time. So it is important to program an effective recognition system to identify positive cases. In this paper, the author proposes an ensemble deep learning model, combining two state-of-art pre-trained models as ResNet-152 and DenseNet-121 to identify COVID-19 cases. The experimental validation result is immensely well with an accuracy of 98.43% on the proposed model. The author also compares the ensemble model's performance with ResNet-50 and DenseNet-121 separately.