Comparative Bladder Cancer Tissues Prediction Using Vision Transformer.
Bladder 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 u...
| Publicado en: | Journal of Imaging Informatics in Medicine Vol. 38; no. 3; pp. 1722 - 1734 |
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| Autores principales: | , , , , , |
| Formato: | equations & formulas pictorial research tables/charts Journal Article |
| Publicado: |
Springer Nature
Jun2025
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=185280499&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 185280499 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 29482925 NR3A jtl: Journal of Imaging Informatics in Medicine issn: 29482925 maglogo: N pubinfo: dt: Jun2025 vid: 38 iid: 3 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 185280499 185280499 185280499 10.1007/s10278-024-01228-1 185280499 ppf: 1722 ppct: 12 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Comparative Bladder Cancer Tissues Prediction Using Vision Transformer. aug: au: Sunnetci, Kubilay Muhammed Oguz, Faruk Enes Ekersular, Mahmut Nedim Gulenc, Nadide Gulsah Ozturk, Mahmut Alkan, Ahmet affil: https://ror.org/03h8sa373 Department of Electrical and Electronics Engineering, Osmaniye Korkut Ata University, Osmaniye, Turkey sug: subj: Bladder Neoplasms Diagnosis Data Management Deep Learning Image Interpretation, Computer Assisted Diagnosis, Computer Assisted Methods Convolutional Neural Networks Machine Learning Human Validity Graphical User Interface Decision Support Systems, Management Experimental Studies Sensitivity and Specificity Reliability and Validity Precision False Positive Results Algorithms ab: Bladder 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. pubtype: Academic Journal doctype: equations & formulas pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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