Multi-Classification Network for Identifying COVID-19 Cases Using Deep Convolutional Neural Networks

摘要

The novel coronavirus 2019(COVID-19)rapidly spreading around the world and turns into a pandemic situation,consequently,detecting the coronavirus(COVID-19)affected patients are now the most critical task for medical specialists.The deficiency of medical testing kits leading to huge complexity in detecting COVID-19 patients worldwide,resulting in the number of infected cases is expanding.Therefore,a significant study is necessary about detecting COVID-19 patients using an automated diagnosis method,which hinders the spreading of coronavirus.In this paper,the study suggests a Deep Convolutional Neural Network-based multi-classification framework(COV-MCNet)using eight different pre-trained architectures such as VGG16,VGG19,ResNet50V2,DenseNet201,InceptionV3,MobileNet,InceptionResNetV2,Xception which are trained and tested on the X-ray images of COVID-19,Normal,Viral Pneumonia,and Bacterial Pneumonia.The results from 4-class(Normal vs.COVID-19 vs.Viral Pneumonia vs.Bacterial Pneumonia)demonstrated that the pre-trained model DenseNet201 provides the highest classification performance(accuracy:92.54%,precision:93.05%,recall:92.81%,F1-score:92.83%,specificity:97.47%).Notably,the DenseNet201(4-class classification)pre-trained model in the proposed COV-MCNet framework showed higher accuracy compared to the rest seven models.Important to mention that the proposed COV-MCNet model showed comparatively higher classification accuracy based on the small number of pre-processed datasets that specifies the designed system can produce superior results when more data become available.The proposed multi-classification network(COV-MCNet)significantly speeds up the existing radiology based method which will be helpful for the medical community and clinical specialists to early diagnosis the COVID-19 cases during this pandemic.