An Efficient CNN-Based Hybrid Classification and Segmentation Approach for COVID-19 Detection
作者机构:Department of Information TechnologyCollege of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh 84428Saudi Arabia Department of Electronics and Electrical CommunicationsFaculty of Electronic EngineeringMenoufia UniversityMenouf 32952Egypt Department of Industrial Electronics and Control EngineeringFaculty of Electronic EngineeringMenoufia UniversityMenouf 32952Egypt Department of Electronics and Communications EngineeringFaculty of EngineeringZagazig UniversityZagazigSharqia 44519Egypt
出 版 物:《Computers, Materials & Continua》 (计算机、材料和连续体(英文))
年 卷 期:2022年第70卷第3期
页 面:4393-4410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)] 0710[理学-生物学] 1002[医学-临床医学] 1001[医学-基础医学(可授医学、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)] 0812[工学-计算机科学与技术(可授工学、理学学位)] 0702[理学-物理学]
基 金:The authors extend their appreciation to the Deputyship for Research&Innovation Ministry of Education in Saudi Arabia for funding this research work through the project Number(PNU-DRI-Targeted-20-027)
主 题:COVID-19 segmentation classification CNN SPCNN CT images
摘 要:COVID-19 remains to proliferate precipitously in the *** has significantly influenced public health,the world economy,and the persons’***,there is a need to speed up the diagnosis and precautions to deal with COVID-19 *** this explosion of this pandemic,there is a need for automated diagnosis tools to help specialists based onmedical *** paper presents a hybrid Convolutional Neural Network(CNN)-based classification and segmentation approach for COVID-19 detection from Computed Tomography(CT)*** proposed approach is employed to classify and segment the COVID-19,pneumonia,and normal CT *** classification stage is firstly applied to detect and classify the input medical CT ***,the segmentation stage is performed to distinguish between pneumonia and COVID-19 CT *** classification stage is implemented based on a simple and efficient CNN deep learning *** model comprises four Rectified Linear Units(ReLUs),four batch normalization layers,and four convolutional(Conv)*** layer depends on filters with sizes of 64,32,16,and 8.A2×2windowand a stride of 2 are employed in the utilized four max-pooling layers.A soft-max activation function and a Fully-Connected(FC)layer are utilized in the classification stage to perform the detection *** the segmentation process,the Simplified Pulse Coupled Neural Network(SPCNN)is utilized in the proposed hybrid *** proposed segmentation approach is based on salient object detection to localize the COVID-19 or pneumonia region,*** summarize the contributions of the paper,we can say that the classification process with a CNN model can be the first stage a highly-effective automated diagnosis *** the images are accepted by the system,it is possible to perform further processing through a segmentation process to isolate the regions of interest in the *** region of interest can be assesses both automatically and through ***