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Medical Image Analysis Using Deep Learning and Distribution Pattern Matching Algorithm

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dc.contributor.authorJaber M.M.en_US
dc.contributor.authorYussof S.en_US
dc.contributor.authorElameer A.S.en_US
dc.contributor.authorWeng L.Y.en_US
dc.contributor.authorAbd S.K.en_US
dc.contributor.authorNayyar A.en_US
dc.contributor.authorid56519461300en_US
dc.contributor.authorid16023225600en_US
dc.contributor.authorid57221869109en_US
dc.contributor.authorid26326032700en_US
dc.contributor.authorid56516784600en_US
dc.contributor.authorid55201442200en_US
dc.date.accessioned2023-05-29T09:41:16Z
dc.date.available2023-05-29T09:41:16Z
dc.date.issued2022
dc.descriptionAutomation; Complex networks; Computational complexity; Deep learning; Image analysis; Medical imaging; Pattern matching; Pixels; Distribution pattern-matching rule; Distribution patterns; Gray wolf-optimized deep convolution network; Gray wolves; Learning patterns; Matching rules; Medical fields; Medical image analysis; Pattern matching algorithms; Pattern-matching; Convolutionen_US
dc.description.abstractArtificial intelligence plays an essential role in the medical and health industries. Deep convolution networks offer valuable services and help create automated systems to perform medical image analysis. However, convolution networks examine medical images effectively; such systems require high computational complexity when recognizing the same disease-affected region. Therefore, an optimized deep convolution network is utilized for analyzing disease-affected regions in this work. Different disease-relatedmedical images are selected and examined pixel by pixel; this analysis uses the gray wolf optimized deep learning network. This method identifies affected pixels by the gray wolf hunting process. The convolution network uses an automatic learning function that predicts the disease affected by previous imaging analysis. The optimized algorithm-based selected regions are further examined using the distribution pattern-matching rule. The pattern-matching process recognizes the disease effectively, and the system's efficiency is evaluated using the MATLAB implementation process. This process ensures high accuracy of up to 99.02% to 99.37% and reduces computational complexity. � 2022 Tech Science Press. All rights reserved.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.32604/cmc.2022.023387
dc.identifier.epage2190
dc.identifier.issue2
dc.identifier.scopus2-s2.0-85127317573
dc.identifier.spage2175
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85127317573&doi=10.32604%2fcmc.2022.023387&partnerID=40&md5=5cb751fb30918781006e5281000059be
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/27227
dc.identifier.volume72
dc.publisherTech Science Pressen_US
dc.relation.ispartofAll Open Access, Gold
dc.sourceScopus
dc.sourcetitleComputers, Materials and Continua
dc.titleMedical Image Analysis Using Deep Learning and Distribution Pattern Matching Algorithmen_US
dc.typeArticleen_US
dspace.entity.typePublication
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