Deep transfer learning methods for classification colorectal cancer based on histology images

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dc.contributor.authorAlhanaf, Ahmed Sami
dc.contributor.authorAl-Jumaili, Saif
dc.contributor.authorBilgin, Gökhan
dc.contributor.authorDuru, Adil Deniz
dc.contributor.authorAlyassri, Salam
dc.contributor.authorBalık, Hasan Hüseyin
dc.date.accessioned2022-12-27T16:47:10Z
dc.date.available2022-12-27T16:47:10Z
dc.date.issued2022en_US
dc.departmentEnstitüler, Lisansüstü Eğitim Enstitüsü, Elektrik ve Bilgisayar Mühendisliği Ana Bilim Dalıen_US
dc.description.abstractDeep transfer learning is one of the common techniques used to classify different types of cancer. The goal of this research is to focus on and adopt a fast, accurate, suitable, and reliable for classification of colorectal cancer. Digital histology images are adjustable to the application of convolutional neural networks (CNNs) for analysis and classification, due to the sheer size of pixel data present in them. Which can provide a lot of information about colorectal cancer. We used ten different types of pr-trained models with two type method of classification techniques namely (normal classification and k-fold crosse validation) to classify the tumor tissue, we used two different kinds of datasets were these datasets consisting of three classes (normal, low tumors, and high tumors). Among all these eight models of deep transfer learning, the highest accuracy achieved was 96.6% with Darknet53 for 5-Fold and for normal classification the highest results obtained was 98.7% for ResNet50. Moreover, we compared our result with many other papers in stat-of-the-art, the results obtained show clearly the proposed method was outperformed the other papers.en_US
dc.identifier.citationAlhanaf, A. S., Al–jumaili, S., Bilgin, G., Duru, A. D., Alyassri, S., Balık, H. H. (2022). Deep transfer learning methods for classification colorectal cancer based on histology images. In 2022 International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT) (pp. 818-821). IEEE.en_US
dc.identifier.endpage821en_US
dc.identifier.isbn9781665470131
dc.identifier.scopus2-s2.0-85142791512
dc.identifier.scopusqualityN/A
dc.identifier.startpage818en_US
dc.identifier.urihttps://hdl.handle.net/20.500.12939/3162
dc.indekslendigikaynakScopus
dc.institutionauthorAl-Jumaili, Saif
dc.institutionauthorAlyassri, Salam
dc.language.isoen
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.relation.ispartofISMSIT 2022 - 6th International Symposium on Multidisciplinary Studies and Innovative Technologies, Proceedings
dc.relation.isversionof10.1109/ISMSIT56059.2022.9932746en_US
dc.relation.publicationcategoryKonferans Öğesi - Ulusal - İdari Personel ve Öğrencien_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectClassificationen_US
dc.subjectCNNen_US
dc.subjectColorectal Canceren_US
dc.subjectDeep Learningen_US
dc.subjectTransfer Learningen_US
dc.titleDeep transfer learning methods for classification colorectal cancer based on histology images
dc.typeConference Object

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