An Efficient CNN-Based Hybrid Classification and Segmentation Approach for COVID-19 Detection

作     者：Abeer D.Algarni Walid El-Shafai Ghada M.El Banby Fathi E.Abd El-Samie Naglaa F.Soliman 

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 world.It has significantly influenced public health,the world economy,and the persons’lives.Hence,there is a need to speed up the diagnosis and precautions to deal with COVID-19 patients.With this explosion of this pandemic,there is a need for automated diagnosis tools to help specialists based onmedical images.This paper presents a hybrid Convolutional Neural Network(CNN)-based classification and segmentation approach for COVID-19 detection from Computed Tomography(CT)images.The proposed approach is employed to classify and segment the COVID-19,pneumonia,and normal CT images.The classification stage is firstly applied to detect and classify the input medical CT images.Then,the segmentation stage is performed to distinguish between pneumonia and COVID-19 CT images.The classification stage is implemented based on a simple and efficient CNN deep learning model.This model comprises four Rectified Linear Units(ReLUs),four batch normalization layers,and four convolutional(Conv)layers.TheConv 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 process.For the segmentation process,the Simplified Pulse Coupled Neural Network(SPCNN)is utilized in the proposed hybrid approach.The proposed segmentation approach is based on salient object detection to localize the COVID-19 or pneumonia region,accurately.To 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 system.Once 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 images.The region of interest can be assesses both automatically and through experts.This strategy helps somuch in saving the time and efforts of specialists with the explosion of COVID-19 pandemic in the world.The proposed classification approach is applied for different scenarios of 80%,70%,or 60%of the data for training and 20%,30,or 40%of the data for testing,respectively.In these scenarios,the proposed approach achieves classification accuracies of 100%,99.45%,and 98.55%,respectively.Thus,the obtained results demonstrate and prove the efficacy of the proposed approach for assisting the specialists in automated medical diagnosis services.