A Novel Explainable CNN Model for Screening COVID-19 on X-ray Images

作     者：Hicham Moujahid Bouchaib Cherradi Oussama El Gannour Wamda Nagmeldin Abdelzahir Abdelmaboud Mohammed Al-Sarem Lhoussain Bahatti Faisal Saeed Mohammed Hadwan 

作者机构：EEIS LaboratoryENSET of MohammediaHassan II University of CasablancaMohammedia28820Morocco STIE TeamCRMEF Casablanca-SettatThe Provincial Section of El JadidaEl Jadida24000Morocco Department of Information SystemsCollege of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAl-Kharj11942Saudi Arabia Department of Information SystemsKing Khalid UniversityMuhayel Aseer61913Saudi Arabia College of Computer Science and EngineeringTaibah UniversityMedina42353Saudi Arabia Department of Computer ScienceUniversity of Sheba RegionMarib14400Yemen DAAI Research GroupDepartment of Computing and Data ScienceSchool of Computing and Digital TechnologyBirmingham City UniversityBirminghamB47XGUK Department of Information TechnologyCollege of ComputerQassim UniversityBuraydah 51452Saudi Arabia Department of Computer ScienceCollege of Applied SciencesTaiz UniversityTaiz6803Yemen 

出 版 物：《Computer Systems Science & Engineering》 (计算机系统科学与工程（英文）)

年 卷 期：2023年第46卷第8期

页      面：1789-1809页

核心收录：

学科分类：08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：This research is funded by the Deanship of Scientific Research at King Khalid University through Large Groups.(Project under grant number(RGP.2/111/43)) 

主　　题：Artificial intelligence intelligent diagnostic systems decisionmaking COVID-19 convolutional neural network 

摘      要：Due to the rapid propagation characteristic of the Coronavirus(COVID-19)disease,manual diagnostic methods cannot handle the large number of infected individuals to prevent the spread of ***,new automated diagnostic methods have been brought on board,particularly methods based on artificial intelligence using different medical data such as X-ray *** imaging,for example,produces several image types that can be processed and analyzed by machine and deep learning methods.X-ray imaging materials widely exist in most hospitals and health institutes since they are affordable compared to other imaging *** this paper,we propose a novel Convolutional Neural Network(CNN)model(COV2Net)that can detect COVID-19 virus by analyzing the X-ray images of suspected *** model is trained on a dataset containing thousands of X-ray images collected from different *** model was tested and evaluated on an independent *** order to approve the performance of the proposed model,three CNN models namely Mobile-Net,Residential Energy Services Network(Res-Net),and Visual Geometry Group 16(VGG-16)have been implemented using transfer learning *** experiment consists of a multi-label classification task based on X-ray images for normal patients,patients infected by COVID-19 virus and other patients infected with *** proposed model is empowered with Gradient-weighted Class Activation Mapping(Grad-CAM)and Grad-Cam++techniques for a visual explanation and methodology debugging *** finding results show that the proposed model COV2Net outperforms the state-of-the-art methods.