A Novel Automated Method for COVID-19 Infection and Lung Segmentation using Deep Neural Networks

Abstract

The COVID-19 pandemic first originated in Wuhan, China and has spread to every country in the world. Without a viable cure in the near future, there is an urgent need for rapid diagnosis of COVID-19, faster test results and automated segmentation of infected region in the lungs. The aim of this paper is to assist in the rapid detection and segmentation of COVID-19 patients using deep learning techniques. This paper proposes a method for automatic segmentation of the lung and infected regions of COVID 19 patients using lung CT scan dataset. This has been done using a modified U-Net model along with different cross validation folds. The region of infection which is segmented will contain the lesion, which if identified in the early stages can be beneficial during treatment of the person. This can help doctors to determine the severity of the infection and suggest treatments based on it. A comparative analysis of the proposed architectures has been done against recently published results which proves the superiority of our models in terms of dice similarity coefficients.