Automated Detection of Covid-19 Coronavirus Cases Using Deep Neural Networks with CT Images

Abstract

Early detection of COVID-19 based on chest CT will enable timely treatment of patients and help control the spread of the disease. With rapid spreading of COVID-19 in many countries, however, CT volumes of suspicious patients are increasing at a speed much faster than the availability of human experts. Here, we propose an artificial intelligence (AI) system for fast COVID-19 diagnosis with an accuracy comparable to experienced radiologists. A large dataset was constructed by collecting 746 CT volumes of 394 patients with confirmed COVID-19 and 397 negative cases from publicly available chest CT datasets. In this paper, we propose a deep learning architecture to detect Covid-19 Coronavirus in CT Images. This architecture contains one network to classify images as either Covid or Non-Covid. The experiment results evaluated by three parameters including accuracy, sensitivity, and specificity. For the ResNet-50 deep learning, these outcomes refer to the maximum sensitivity being 92.6% by the training dataset for the ResNet-50. ResNet-50 can be considered as a high sensitivity model to characterize and diagnose Covid-19 Coronavirus, and can be used as an adjuvant tool in radiology departments.