Diagnosis of Oral Squamous Cell Carcinoma Using Deep Neural Networks and Binary Particle Swarm Optimization on Histopathological Images: An AIoMT Approach

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dc.authoridhttps://orcid.org/0000-0002-4388-1480en_US
dc.authoridhttps://orcid.org/0000-0001-8028-7918en_US
dc.authoridhttps://orcid.org/0000-0003-4492-2181en_US
dc.authoridhttps://orcid.org/0000-0002-2029-5067en_US
dc.authoridhttps://orcid.org/0000-0002-2881-8635en_US
dc.contributor.authorDeif, Mohanad A.
dc.contributor.authorAttar, Hani
dc.contributor.authorAmer, Ayman
dc.contributor.authorElhaty, Ismail A. M.
dc.contributor.authorKhosravi, Mohammad R.
dc.contributor.authorSolyman, Ahmad Amin Ahmad
dc.date.accessioned2023-11-02T06:33:49Z
dc.date.available2023-11-02T06:33:49Z
dc.date.issued2022en_US
dc.departmentSağlık Bilimleri Fakültesien_US
dc.description.abstractOverall prediction of oral cavity squamous cell carcinoma (OCSCC) remains inadequate, as more than half of patients with oral cavity cancer are detected at later stages. It is generally accepted that the differential diagnosis of OCSCC is usually difficult and requires expertise and experience. Diagnosis from biopsy tissue is a complex process, and it is slow, costly, and prone to human error. To overcome these problems, a computer-aided diagnosis (CAD) approach was proposed in this work. A dataset comprising two categories, normal epithelium of the oral cavity (NEOR) and squamous cell carcinoma of the oral cavity (OSCC), was used. Feature extraction was performed from this dataset using four deep learning (DL) models (VGG16, AlexNet, ResNet50, and Inception V3) to realize artificial intelligence of medial things (AIoMT). Binary Particle Swarm Optimization (BPSO) was used to select the best features. The effects of Reinhard stain normalization on performance were also investigated. After the best features were extracted and selected, they were classified using the XGBoost. The best classification accuracy of 96.3% was obtained when using Inception V3 with BPSO. This approach significantly contributes to improving the diagnostic efficiency of OCSCC patients using histopathological images while reducing diagnostic costs.en_US
dc.identifier.doi10.1155/2022/6364102en_US
dc.identifier.endpage13en_US
dc.identifier.issn1687-5265
dc.identifier.issn1687-5273
dc.identifier.pmid36210968en_US
dc.identifier.scopus2-s2.0-85139408682en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.startpage1en_US
dc.identifier.urihttps://hdl.handle.net/11363/6175
dc.identifier.urihttps://doi.org/
dc.identifier.volume2022en_US
dc.identifier.wosWOS:000869058600001en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakPubMeden_US
dc.institutionauthorElhaty, Ismail A. M.
dc.language.isoenen_US
dc.publisherHINDAWI LTD, ADAM HOUSE, 3RD FLR, 1 FITZROY SQ, LONDON W1T 5HF, ENGLANDen_US
dc.relation.ispartofComputational Intelligence and Neuroscienceen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 United States*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/us/*
dc.titleDiagnosis of Oral Squamous Cell Carcinoma Using Deep Neural Networks and Binary Particle Swarm Optimization on Histopathological Images: An AIoMT Approachen_US
dc.typeArticleen_US

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