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.

A Multipulse Radar Signal Recognition Approach via HRF‐Net Deep Learning Models

“…In semantic segmentation, CNN can extract image features and achieve image pixel-level classification [ 13 ]. In order to improve the recognition effect, the image can be preprocessed, such as superresolution reconstruction [ 14 ], and attention mechanism can also be introduced to improve the performance of the network [ 15 ]. AlexNet [ 16 ] applies ReLU, LRN [ 17 ], and Dropout [ 18 ] at the same time.…”

Detecting COVID-19 Patients in X-Ray Images Based on MAI-Nets

“…When detecting COVID-19, Ali Abbasian Ardakani et al [28] used 10 famous convolutional neural networks (CNNs) to distinguish COVID-19 patients from non-COVID-19 patients, and summarized the characteristics of these famous networks. The CFW-NET proposed by Wang et al [29] has achieved an overall accuracy rate of 94.35% for COVID-19 detection.…”

Detecting COVID‐19 in Chest X‐Ray Images via MCFF‐Net

“…At the same time, many outstanding research results have emerged in this field. Based on the characteristics of CXR images, Wang et al [ 10 ] designed the Channel Feature Weight Extraction module (CFWE) and proposed a new network structure CFW-Net on this basis, which has achieved a good classification effect. Wang et al [ 11 ] designed a Multiattention Interaction Enhancement module (MAIE) and proposed a new convolutional neural network, MAI-Net.…”