Breast Cancer Mass Classification Using Machine Learning, Binary-Coded Genetic Algorithms and an Ensemble of Deep Transfer Learning

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dc.authoridTIRYAKI, VOLKAN MUJDAT/0000-0003-1824-5260
dc.contributor.authorTiryaki, Volkan Mujdat
dc.contributor.authorTutkun, Nedim
dc.date.accessioned2024-12-24T19:28:29Z
dc.date.available2024-12-24T19:28:29Z
dc.date.issued2023
dc.departmentSiirt Üniversitesi
dc.description.abstractThe diagnosis of breast cancer (BC) as early as possible is crucial for increasing the survival rate. Mammography enables finding the breast tissue changes years before they could develop into cancer symptoms. In this study, machine learning methods for BC mass pathology classification have been investigated using the radiologists' mass annotations on the screen-film mammograms of the Breast Cancer Digital Repository (BCDR). The performances of precomputed features in the BCDR and discrete wavelet transform followed by Radon transform have been investigated by using four sequential feature selections and three genetic algorithms. Feature fusion from craniocaudal and mediolateral oblique views was shown to increase the performance of the classifier. Mass classification has been implemented by deep transfer learning (DTL) using the weights of ResNet50, NASNetLarge and Xception networks. An ensemble of DTL (EDTL) was shown to have higher classification performance than the DTL models. The proposed EDTL has area under the receiver operating curve (AUC) scores of 0.8843 and 0.9089 for mass classification on the region of interest (ROI) and ROI union datasets, respectively. The proposed EDTL has the highest BC mass classification AUC score on the BCDR to date and may be useful for other datasets.
dc.description.sponsorshipSiirt University [2021-SIUEMUEH-01]
dc.description.sponsorshipSiirt University Scientific Research Projects Directorate [Grant number 2021-SIUEMUEH-01, to V.M.T.]
dc.identifier.doi10.1093/comjnl/bxad046
dc.identifier.endpage1125
dc.identifier.issn0010-4620
dc.identifier.issn1460-2067
dc.identifier.issue3
dc.identifier.scopus2-s2.0-85190813027
dc.identifier.scopusqualityQ2
dc.identifier.startpage1111
dc.identifier.urihttps://doi.org/10.1093/comjnl/bxad046
dc.identifier.urihttps://hdl.handle.net/20.500.12604/7087
dc.identifier.volume67
dc.identifier.wosWOS:000976553600001
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherOxford Univ Press
dc.relation.ispartofComputer Journal
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_20241222
dc.subjectmammography
dc.subjectcomputer-aided diagnosis
dc.subjectnodule
dc.subjectpathology
dc.subjectradiomics
dc.titleBreast Cancer Mass Classification Using Machine Learning, Binary-Coded Genetic Algorithms and an Ensemble of Deep Transfer Learning
dc.typeArticle

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