Detecting COVID-19 Patients in X-Ray Images Based on MAI-Nets
Wei Wang, Xiao Huang, Li Ji, Peng Zhang, Xin Wang
Abstract
COVID-19 is an infectious disease caused by virus SARS-CoV-2 virus.Early classification of COVID-19 is essential for disease cure and control.Transcription-polymerase chain reaction (RT-PCR) is used widely for the detection of COVID-19.However, its high cost, time-consuming and low sensitivity will significantly reduce the diagnosis efficiency and increase the difficulty of diagnosis for COVID-19.For X-ray images of COVID-19 patients have high inter-class similarity and low intra-class variability, we specifically designed a multi attention interaction enhancement module (MAIE) and proposed a new convolutional neural network, MAI-Net, based on this module.As a lightweight network, MAI-Net has fewer layers and amount of network parameters than other network models, enabling more efficient detection of COVID-19.To verify the performance of the model, MAI-Net performed a comparison experiment on two open-source datasets.The experimental results show that its overall accuracy and COVID-19 category accuracy are 96.42% and 100%, respectively, and the sensitivity of COVID-19 is 99.02%.Considering the factors such as accuracy rate, the parameters number of network model and the calculation amount, MAI-Net has better practicability.Compared with the existing work, the network structure of MAI-Net is simpler, and the hardware requirements of the equipment are lower, which can be better used in ordinary equipment.