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Identifying Severity of cOVID-19 Medical Images by categorizing Using HSDc Model

computer Systems Science & Engineering 2023年第10期47卷 613-635页

作者： K.Ravishankar c.jothikumar Department of computing Technologies School of ComputingSRM Institute of Science and TechnologyChengalpattuTamilnadu603203India

Since cOVID-19 infections are increasing all over the world,there is a need for developing solutions for its early and accurate diagnosis is a *** for cOVID-19 include screeningmethods like chest X-rays and computed Tomography(cT)*** work must be done on preprocessing the datasets,such as eliminating the diaphragm portions,enhancing the image intensity,and minimizing *** addition to the detection of cOVID-19,the severity of the infection needs to be *** HSDc model is proposed to solve these problems,which will detect and classify the severity of cOVID-19 from X-ray and cT-scan *** cT-scan images,the histogram threshold of the input image is adaptively determined using the IcH Swarm Optimization Segmentation(IcHSeg)*** on the Statistical and Shape-based feature vectors(FVs),the extracted regions are classified using a Hybrid model for cT images(HSDccT)*** the infections are detected,it’s classified as Normal,Moderate,and Severe.A fused FHI is formed for X-ray images by extracting the features of Histogram-oriented gradient(HOG)and Image profile(IP).The FHI features of X-ray images are classified using Hybrid Support Vector Machine(SVM)and Deep convolutional Neural Network(DcNN)HSDcX algorithm into cOVID-19 or else Pneumonia,or *** results have shown that the accuracy of the HSDc model attains the highest of 94.6 for cT-scan images and 95.6 for X-ray images when compared to SVM and *** study thus significantly helps medical professionals and doctors diagnose cOVID-19 infections quickly,which is the most needed in current years.

关键词： cT-scan convolution neural network(cNN) deep cNN(HSDc) hybrid support vector machine(SVM) improved chicken swarmoptimization(IcHO) cOVID-19 and image profile(IP)

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