Lightweight Transfer Learning Models for Ultrasound-Guided Classification of COVID-19 Patients

作     者：Mohamed Esmail Karar Omar Reyad Mohammed Abd-Elnaby Abdel-Haleem Abdel-Aty Marwa Ahmed Shouman 

作者机构：College of Computing and Information TechnologyShaqra UniversityShaqra11961Saudi Arabia Department of Industrial Electronics and Control EngineeringFaculty of Electronic Engineering(FEE)Menoufia UniversityMenouf32952Egypt Department of Mathematics and Computer ScienceFaculty of ScienceSohag UniversitySohag82524Egypt Department of Computer EngineeringCollege of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Department of PhysicsCollege of SciencesUniversity of BishaBisha61922Saudi Arabia Department of PhysicsFaculty of ScienceAl-Azhar UniversityAssiut71524Egypt Department of Computer Science and EngineeringFaculty of Electronic Engineering(FEE)Menoufia UniversityMenouf32952Egypt 

出 版 物：《Computers, Materials & Continua》 (计算机、材料和连续体（英文）)

年 卷 期：2021年第69卷第11期

页      面：2295-2312页

核心收录：

学科分类：0831[工学-生物医学工程（可授工学、理学、医学学位）] 0808[工学-电气工程] 0809[工学-电子科学与技术（可授工学、理学学位）] 08[工学] 0805[工学-材料科学与工程（可授工学、理学学位）] 0701[理学-数学] 0801[工学-力学（可授工学、理学学位）] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：This research received the support from Taif University Researchers Supporting Project Number(TURSP-2020/147) Taif university Taif Saudi Arabia. 

主　　题：Coronavirus medical image processing artificial intelligence ultrasound 

摘      要：Lightweight deep convolutional neural networks(CNNs)present a good solution to achieve fast and accurate image-guided diagnostic procedures of COVID-19 patients.Recently,advantages of portable Ultrasound(US)imaging such as simplicity and safe procedures have attracted many radiologists for scanning suspected COVID-19 cases.In this paper,a new framework of lightweight deep learning classifiers,namely COVID-LWNet is proposed to identify COVID-19 and pneumonia abnormalities in US images.Compared to traditional deep learning models,lightweight CNNs showed significant performance of real-time vision applications by using mobile devices with limited hardware resources.Four main lightweight deep learning models,namely MobileNets,ShuffleNets,MENet and MnasNet have been proposed to identify the health status of lungs using US images.Public image dataset(POCUS)was used to validate our proposed COVID-LWNet framework successfully.Three classes of infectious COVID-19,bacterial pneumonia,and the healthy lung were investigated in this study.The results showed that the performance of our proposed MnasNet classifier achieved the best accuracy score and shortest training time of 99.0%and 647.0 s,respectively.This paper demonstrates the feasibility of using our proposed COVID-LWNet framework as a new mobilebased radiological tool for clinical diagnosis of COVID-19 and other lung diseases.