Automated Model for Tracking COVID-19 Infected Cases till Final Diagnosis

The COVID-19 pandemic is now devastating It affects public safety and well-being A crucial step in the COVID-19 battle will be tracking the positive cases with convenient accuracy of diagnosis However, the time of pandemics shows the emergent need for automated diagnosis to support medical staff decisions in different steps of diagnosis and prognosis of target disease like medical imaging through X-rays, CT-Scans, etc Besides laboratory investigation steps, we propose a system that provides an automated multi-stage decision system supported with decision causes using deep learning techniques for tracking cases of a target disease (COVID-19 in our paper) Encouraged by the open-source Data sets for COVID-19 infected patients’ chest radiology, we proposed a system of three Consecutive stages Each stage consists of a deep learning binary classifier tailored for the detection of a specific COVID-19 infection feature from chest radiology, either X-ray or CT-scan By integrating the three classifiers, a multi-stage diagnostic system was attained that achieves an accuracy of (87 980 %), (78 717%), and (84%) for the three stages, respectively By no means a production-ready solution, our system will help in reducing errors caused by human decisions, taken under pressure, and exhausting routines, and it will be reliable to take urgent decisions once the model performance achieves the needed accuracy Copyright © 2021 by SCITEPRESS – Science and Technology Publications, Lda All rights reserved