Comparative Bladder Cancer Tissues Prediction Using Vision Transformer

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dc.authoridOzturk, Mahmut/0000-0003-2600-7051
dc.authoridEkersular, Mahmut Nedim/0000-0002-0209-9484
dc.authoridALKAN, Ahmet/0000-0003-0857-0764
dc.contributor.authorSunnetci, Kubilay Muhammed
dc.contributor.authorOguz, Faruk Enes
dc.contributor.authorEkersular, Mahmut Nedim
dc.contributor.authorGulenc, Nadide Gulsah
dc.contributor.authorOzturk, Mahmut
dc.contributor.authorAlkan, Ahmet
dc.date.accessioned2025-04-06T12:23:56Z
dc.date.available2025-04-06T12:23:56Z
dc.date.issued2024
dc.departmentTekirdağ Namık Kemal Üniversitesi
dc.description.abstractBladder cancer, often asymptomatic in the early stages, is a type of cancer where early detection is crucial. Herein, endoscopic images are meticulously evaluated by experts, and sometimes even by different disciplines, to identify tissue types. It is believed that the time spent by experts can be utilized for patient treatment with the creation of a computer-aided decision support system. For this purpose, in this study, it is evaluated that the performances of three models proposed using the bladder tissue dataset. The first model is a convolutional neural network (CNN)-based deep learning (DL) network, and the second is a model named hybrid cnn-machine learning (ML) or DL + ML, which involves classifying deep features obtained from a CNN-based network with ML. The last one, and the one that achieved the best performance metrics, is a vision transformer (ViT) architecture. Furthermore, a graphical user interface (GUI) is provided for an accessible decision support system. As a result, accuracy and F1 score values for DL, DL + ML, and ViT models are 0.9086-0.8971-0.9257 and 0.8884-0.8496-0.8931, respectively.
dc.identifier.doi10.1007/s10278-024-01228-1
dc.identifier.issn2948-2925
dc.identifier.issn2948-2933
dc.identifier.pmid39455543
dc.identifier.scopusqualityN/A
dc.identifier.urihttps://doi.org/10.1007/s10278-024-01228-1
dc.identifier.urihttps://hdl.handle.net/20.500.11776/17267
dc.identifier.wosWOS:001342159200002
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakPubMed
dc.language.isoen
dc.publisherSpringer
dc.relation.ispartofJournal of Imaging Informatics In Medicine
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_WOS_20250406
dc.subjectBladder cancer
dc.subjectClassification
dc.subjectDeep learning
dc.subjectMachine learning
dc.subjectVision transformer
dc.titleComparative Bladder Cancer Tissues Prediction Using Vision Transformer
dc.typeArticle

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