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

Gomaa, M. A.; Wassel, M.; Abdelmawla, R. M.; Ibrahim, N.; Nasser, K.; Osman, N. A.; Gomaa, W.

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 productionready 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.