Segmented-Based and Segmented-Free Approach for COVID-19 Detection

Abstract

According to WHO, lung infection is one of the most serious problems across the world, especially for children under five years old and older people over sixteen years old. In this study, we designed a deep learning-based model to aid medical practitioners in their diagnostic process. Here, U-Net based segmentation framework is considered to get the region of interest (ROI) of the lung area from the chest x-ray images. Two standard deep learning models and a developed CNN model comprise this framework. A deep ensemble framework method is presented to detect COVID-19 disease from a collection of chest X-ray images of disparate cases in both segment-free and segmented-based lung images. Different public datasets were used for segmentation and classification to test the system’s robustness. The performance of segmentation and classification approaches returns promising outcomes compared to the state-of-the-art.