Classification of Gastrointestinal Diseases in Endoscopic Images: Comparative Analysis of Convolutional Neural Networks and Vision Transformers

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dc.contributor.authorAyan, Enes
dc.date.accessioned2025-01-21T16:11:42Z
dc.date.available2025-01-21T16:11:42Z
dc.date.issued2024
dc.departmentKırıkkale Üniversitesi
dc.description.abstractGastrointestinal (GI) diseases are a major issue in the human digestive system. Therefore, many studies have explored the automatic classification of GI diseases to reduce the burden on clinicians and improve patient outcomes for both diagnosis and treatment purposes. Convolutional neural networks (CNNs) and Vision Transformers (ViTs) in deep learning approaches have become a popular research area for the automatic detection of diseases from medical images. This study evaluated the classification performance of thirteen different CNN models and two different ViT architectures on endoscopic images. The impact of transfer learning parameters on classification performance was also observed. The tests revealed that the classification accuracies of the ViT models were 91.25% and 90.50%, respectively. In contrast, the DenseNet201 architecture, with optimized transfer learning parameters, achieved an accuracy of 93.13%, recall of 93.17%, precision of 93.13%, and an F1 score of 93.11%, making it the most successful model among all the others. Considering the results, it is evident that a well-optimized CNN model achieved better classification performance than the ViT models.
dc.identifier.doi10.21597/jist.1501787
dc.identifier.endpage999
dc.identifier.issn2146-0574
dc.identifier.issn2536-4618
dc.identifier.issue3
dc.identifier.startpage988
dc.identifier.trdizinid1260621
dc.identifier.urihttps://doi.org/10.21597/jist.1501787
dc.identifier.urihttps://search.trdizin.gov.tr/tr/yayin/detay/1260621
dc.identifier.urihttps://hdl.handle.net/20.500.12587/21539
dc.identifier.volume14
dc.indekslendigikaynakTR-Dizin
dc.language.isoen
dc.relation.ispartofIğdır Üniversitesi Fen Bilimleri Enstitüsü Dergisi
dc.relation.publicationcategoryMakale - Ulusal Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_20241229
dc.subjectGenel ve Dahili Tıp
dc.subjectGastroenteroloji ve Hepatoloji
dc.subjectBilgisayar Bilimleri
dc.subjectYapay Zeka
dc.titleClassification of Gastrointestinal Diseases in Endoscopic Images: Comparative Analysis of Convolutional Neural Networks and Vision Transformers
dc.typeArticle

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