Multi-Class gastrointestinal images classification using EfficientNet-B0 CNN model

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dc.contributor.authorUçan, Murat
dc.contributor.authorKaya, Buket
dc.contributor.authorKaya, Mehmet
dc.date.accessioned2024-04-24T17:56:25Z
dc.date.available2024-04-24T17:56:25Z
dc.date.issued2022
dc.departmentDicle Üniversitesi, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümüen_US
dc.description2022 International Conference on Data Analytics for Business and Industry, ICDABI 2022 -- 25 October 2022 through 26 October 2022 -- -- 186761en_US
dc.description.abstractMany diseases and cancerous cells can be detected using images taken by gastroenterology specialists. Accurate and rapid detection of gastroenterological diseases is very important for the treatment processes to be applied and for the patient's recovery. In this study, a data set containing data from 8 different diseases (Esophagitis, Dyed and Lifted Polyps, Dyed Resection Margins, Cecum, Pylorus, Z-line, Polyps, Ulcerative colitis) was used. A deep learning network was trained using the EfficientNet architecture and the test results were given in the study. In addition, comparisons were made with other studies using the same data set and the same parameters in the literature. Studies have shown that gastrological images can be successfully classified with an accuracy of 0.935. Class-based classification results are also shared in detail for 8 diseases in the results section of the study. The results showed that the trained architecture would contribute to minimizing human error in disease detection.en_US
dc.description.sponsorshipFirat Üniversitesi, FU: MMY.22.03en_US
dc.description.sponsorshipACKNOWLEDGEMENT This work was supported by Scientific Research Projects Coordination Unit of Fırat University under Grant No: MMY.22.03en_US
dc.description.sponsorshipThis work was supported by Scientific Research Projects Coordination Unit of Firat University under Grant No: MMY.22.03en_US
dc.identifier.citationUçan, M., Kaya, B. ve Kaya, M. (2022). Multi-Class gastrointestinal images classification using EfficientNet-B0 CNN model. 2022 International Conference on Data Analytics for Business and Industry, ICDABI 2022, 146-150.
dc.identifier.doi10.1109/ICDABI56818.2022.10041447
dc.identifier.endpage150en_US
dc.identifier.isbn9781665490580
dc.identifier.scopus2-s2.0-85149328866en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.startpage146en_US
dc.identifier.urihttps://doi.org/10.1109/ICDABI56818.2022.10041447
dc.identifier.urihttps://hdl.handle.net/11468/23504
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.relation.ispartof2022 International Conference on Data Analytics for Business and Industry, ICDABI 2022en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectClassificationen_US
dc.subjectCnnen_US
dc.subjectConvolutional neural networken_US
dc.subjectDeep learningen_US
dc.subjectEfficientneten_US
dc.subjectGastroenterologyen_US
dc.titleMulti-Class gastrointestinal images classification using EfficientNet-B0 CNN modelen_US
dc.typeConference Objecten_US

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