Using CFW-Net Deep Learning Models for X-Ray Images to Detect COVID-19 Patients

Abstract

COVID-19 is an infectious disease caused by severe acute respiratory syndrome (SARS)-CoV-2 virus. So far, more than 20 million people have been infected. With the rapid spread of COVID-19 in the world, most countries are facing the shortage of medical resources. As the most extensive detection technology at present, reverse transcription polymerase chain reaction (RT-PCR) is expensive, long-time (time consuming) and low sensitivity. These problems prompted us to propose a deep learning model to help radiologists and clinicians detect COVID-19 cases through chest X-ray. According to the characteristics of chest X-ray image, we designed the channel feature weight extraction (CFWE) module, and proposed a new convolutional neural network, CFW-Net, based on the CFWE module. Meanwhile, in order to improve recognition efficiency, the network adopts three classifiers for classification: one fully connected (FC) layers, global average pooling fully-connected (GFC) module and point convolution global average pooling (CGAP) module. The latter two methods have fewer parameters, less calculation and better real-time performance. In this paper, we have evaluated CFW-Net based on two open-source datasets. The experimental results show that the overall accuracy of our model CFW-Net56-GFC is 94.35% and the accuracy and sensitivity of COVID-19 are 100%. Compared with other methods, our method can detect COVID-19 disease more accurately.

Keywords

• COVID-19
• Deep learning
• CFW-Net
• Convolutional neural network
• Chest X-ray images