Design and Implementation of COVID-19 Assistant Diagnostic System Based on Deep Learning

Abstract

In recent years, novel coronavirus pneumonia has spread rapidly around the world due to its strong infectiousness, and the medical systems of related countries are facing huge challenges. As the most intuitive and effective supplementary diagnostic basis for the results of nucleic acid tests, medical imaging screening has gradually become more and more important in epidemic prevention and control. In this context, this paper develops a novel coronavirus pneumonia-auxiliary diagnostic system by using deep learning techniques. This system can help medical staffs to diagnose the condition through X-Ray images quickly. This system builds a sample dataset by collecting lung X-ray images from two datasets and uses a neural network for auxiliary diagnosis training, which achieves an accuracy rate of 98%. Furthermore, two interactive visual interfaces in the form of PC-side applet and Web page are supported in the system, which makes it much easier for medical personnel to operate the system.