A Rapid Deep Learning Computer-Aided Diagnosis to Simultaneously Detect and Classify the Novel COVID-19 Pandemic

Abstract

The novel coronavirus 2019 (COVID-19) becomes recently a global pandemic as declared by the World Health Organization (WHO) in March 2020. COVID-19 rapidly spread and attacked people in more than 200 countries worldwide. The use of artificial intelligence (AI) techniques has become urgent to prevent the exacerbation of the astounding spread of this pernicious disease. This paper presents a novel rapid deep learning computer-aided Diagnosis (CAD) framework for simultaneously detecting and diagnosing the COVID-19 against different respiratory diseases such as pneumonia, atelectasis, cardiomegaly, infiltration, mass, pneumothorax, nodule, and effusion. To develop a useful patient triage system for detecting COVID-19 in early-stage, rapidly extract the visual diagnosis knowledge from the input chest X-ray (CXR) images is extremely required. The proposed CAD framework shows its capability to automatically detect and diagnose COVID-19 with a detection accuracy of 96.31% and a classification accuracy of 97.40%. Meanwhile, the real-time prediction speed of 0.0093 seconds is achieved for a single testing CXR image. To achieve a high accuracy of the knowledge extraction from the entire CXR images with a high prediction speed would represent a key for developing a comprehensive and useful smart patient triage system in hospitals and healthcare systems.