HIERARCHICAL COIVD FEATURE EXTRACTION AND CLASSIFICATION USING OPTIMISED JCS

Abstract

The novel corona virus disease 2019 (COVID-19) has been a severe health issue affecting the respiratory system and spreads very fast from one human to other overall countries. For controlling such disease, limited diagnostics techniques are utilized to identify COVID-19 patients, which are not effective. To hinder the terrific infection of COVID-19, medical radiology imaging is employed as a complementary tool for the RT-PCR test. The above complex circumstances need to detect suspected COVID-19 patients based on routine techniques like chest CT Scans or CT scan analysis immediately through computerized diagnosis systems such as mass detection, segmentation, and classification. JCS system consists of an explainable classification branch to identify the COVID-19 opacifications and a segmentation branch to discover the opacification areas. The classifier is trained on many images with low-cost patient-level annotations and some images with pixel-level annotations for better activation mapping. And the segmentation branch is trained with accurately annotated CT images, performing fine-grained lesion segmentation. By integrating the two models, our JCS system provides informative diagnosis results for COVID-19. The image of the chests is used for mass detection using the deep learning YOLO technique. Here, it identifies the corona virus-affected chest regions from the normal tissue.