Design of COVID-19 Disease Detection Framework for Medical Health Care System

Abstract

Due to practical challenges in data collecting and the efficacy of testing methods, the recognition and diagnosis of COVID-19 using traditional image processing and ML algorithms is a laborious and time-consuming task. Thus, Deep Learning (DL) algorithms are being developed to diagnose COVID-19 instances of pneumonia globally. Due to the fact that deep learning algorithms have recently been developed for clinical diagnosis on image datasets such as brain MRI, chest X-ray, retina, and CT scans with high precision Lately, suggested deep learning techniques have demonstrated limitations, such as their ability to precisely identify a small number of COVID-19-associated pneumonia cases and their symptoms. A reliable forecasting technique for COVID-19 and pneumonia that makes use of CT scans is necessary. Therefore, the emphasis of this study is on the DL-based approach that uses lung CT images to precisely detect and classify the COVID-19 severity level.