An Expert System for COVID-19 Infection Tracking in Lungs Using Image Processing and Deep Learning Techniques.
The proposed method introduces algorithms for the preprocessing of normal, COVID-19, and pneumonia X-ray lung images which promote the accuracy of classification when compared with raw (unprocessed) X-ray lung images. Preprocessing of an image improves the quality of an image increasing the intersec...
| Published in: | BioMed Research International pp. 1 - 18 |
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| Main Authors: | , , , , |
| Format: | diagnostic images pictorial research tables/charts Journal Article |
| Published: |
Wiley-Blackwell
11/13/2021
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=153551434&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 153551434 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 23146133 FT2T jtl: BioMed Research International issn: 23146133 maglogo: N pubinfo: dt: 11/13/2021 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 153551434 153551434 153551434 10.1155/2021/1896762 153551434 ppf: 1 ppct: 17 formats: fmt: @attributes: type: P tig: atl: An Expert System for COVID-19 Infection Tracking in Lungs Using Image Processing and Deep Learning Techniques. aug: au: Subramaniam, Umashankar Subashini, M. Monica Almakhles, Dhafer Karthick, Alagar Manoharan, S. affil: Department of Communications and Networks, Prince Sultan University, 11586 Riyadh, Saudi Arabia sug: subj: Expert Systems COVID-19 Lung Radiography Image Processing, Computer Assisted Methods Deep Learning Methods Human Algorithms Pneumonia X-Rays Diagnostic Imaging Neural Networks (Computer) ab: The proposed method introduces algorithms for the preprocessing of normal, COVID-19, and pneumonia X-ray lung images which promote the accuracy of classification when compared with raw (unprocessed) X-ray lung images. Preprocessing of an image improves the quality of an image increasing the intersection over union scores in segmentation of lungs from the X-ray images. The authors have implemented an efficient preprocessing and classification technique for respiratory disease detection. In this proposed method, the histogram of oriented gradients (HOG) algorithm, Haar transform (Haar), and local binary pattern (LBP) algorithm were applied on lung X-ray images to extract the best features and segment the left lung and right lung. The segmentation of lungs from the X-ray can improve the accuracy of results in COVID-19 detection algorithms or any machine/deep learning techniques. The segmented lungs are validated over intersection over union scores to compare the algorithms. The preprocessed X-ray image results in better accuracy in classification for all three classes (normal/COVID-19/pneumonia) than unprocessed raw images. VGGNet, AlexNet, Resnet, and the proposed deep neural network were implemented for the classification of respiratory diseases. Among these architectures, the proposed deep neural network outperformed the other models with better classification accuracy. pubtype: Academic Journal doctype: diagnostic images pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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