Residual block fully connected DCNN with categorical generalized focal dice loss and its application to Alzheimer's disease severity detection

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dc.authorid0000-0002-7201-6963en_US
dc.authorid0000-0003-1840-9958en_US
dc.contributor.authorAlhudhaif, Adi
dc.contributor.authorPolat, Kemal
dc.date.accessioned2024-07-29T11:16:44Z
dc.date.available2024-07-29T11:16:44Z
dc.date.issued2023en_US
dc.departmentBAİBÜ, Mühendislik Fakültesi, Elektrik Elektronik Mühendisliği Bölümüen_US
dc.descriptionThis work was supported by the Deputyship for Research & Innovation, Ministry of Education in Saudi Arabia through project number (IF-PSAU-2021/01/18563) . The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.en_US
dc.description.abstractBackground. Alzheimer's disease (AD) is a disease that manifests itself with a deteriora-tion in all mental activities, daily activities, and behaviors, especially memory, due to the constantly increasing damage to some parts of the brain as people age. Detecting AD at an early stage is a significant challenge. Various diagnostic devices are used to diagnose AD. Magnetic Resonance Images (MRI) devices are widely used to analyze and classify the stages of AD. However, the time-consuming process of recording the affected areas of the brain in the images obtained from these devices is another challenge. Therefore, conventional techniques cannot detect the early stage of AD.Methods. In this study, we proposed a deep learning model supported by a fusion loss model that includes fully connected layers and residual blocks to solve the above -mentioned challenges. The proposed model has been trained and tested on the publicly available T1-weighted MRI-based KAGGLE dataset. Data augmentation techniques were used after various preliminary operations were applied to the data set.Results. The proposed model effectively classified four AD classes in the KAGGLE dataset. The proposed model reached the test accuracy of 0.973 in binary classification and 0.982 in multi-class classification thanks to experimental studies and provided a superior classification performance than other studies in the literature. The proposed method can be used online to detect AD and has the feature of a system that will help doctors in the decision-making process.en_US
dc.description.sponsorshipDeputyship for Research & Innovation, Ministry of Education in Saudi Arabia [IF-PSAU-2021/01/18563]en_US
dc.identifier.citationAlhudhaif, A., & Polat, K. (2023). Residual block fully connected DCNN with categorical generalized focal dice loss and its application to Alzheimer’s disease severity detection. PeerJ Computer Science, 9, e1599.en_US
dc.identifier.doi10.7717/peerj-cs.1599
dc.identifier.endpage14en_US
dc.identifier.issn2376-5992
dc.identifier.pmid38077566en_US
dc.identifier.scopus2-s2.0-85177445804en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.startpage1en_US
dc.identifier.urihttp://dx.doi.org/10.7717/peerj-cs.1599
dc.identifier.urihttps://hdl.handle.net/20.500.12491/12283
dc.identifier.volume9en_US
dc.identifier.wosWOS:001090141500004en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakPubMeden_US
dc.institutionauthorPolat, Kemal
dc.language.isoenen_US
dc.publisherPeerJ Incen_US
dc.relation.ispartofPeerJ Computer Scienceen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectAlzheimer's Diseaseen_US
dc.subjectCategorical Generalized Focal Dice Lossen_US
dc.subjectDeep Learningen_US
dc.subjectNew Hybrid Modelsen_US
dc.subjectClassificationen_US
dc.titleResidual block fully connected DCNN with categorical generalized focal dice loss and its application to Alzheimer's disease severity detectionen_US
dc.typeArticleen_US

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