BI-RADS categories and breast lesions classification of mammographic images using artificial intelligence diagnostic model

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dc.contributor.authorTürk, Fuat
dc.contributor.authorAkkur, Erkan
dc.contributor.authorEroğul, Osman
dc.date.accessioned2025-01-21T16:36:09Z
dc.date.available2025-01-21T16:36:09Z
dc.date.issued2023
dc.departmentKırıkkale Üniversitesi
dc.description.abstractAccording to BI-RADS criteria, radiologists evaluate mammography images, and breast lesions are classified as malignant or benign. In this retrospective study, an evaluation was made on 264 mammogram images of 139 patients. First, data augmentation was applied, and then the total number of images was increased to 565. Two computer-aided models were then designed to classify breast lesions and BI-RADS categories. The first of these models is the support vector machine (SVM) based model, and the second is the convolutional neural network (CNN) based model. The SVM-based model could classify BI-RADS categories and malignant-benign discrimination with an accuracy rate of 86.42% and 92.59%, respectively. On the other hand, the CNN-based model showed 79.01% and 83.95% accuracy for BI-RADS categories and malignant benign discrimination, respectively. These results showed that a well-designed machine learning-based classification model can give better results than a deep learning model. Additionally, it can be used as a secondary system for radiologists to differentiate breast lesions and BI-RADS lesion categories.
dc.identifier.doi10.14311/NNW.2023.33.023
dc.identifier.endpage432
dc.identifier.issn1210-0552
dc.identifier.issue6
dc.identifier.scopus2-s2.0-85186953015
dc.identifier.scopusqualityQ4
dc.identifier.startpage413
dc.identifier.urihttps://doi.org/10.14311/NNW.2023.33.023
dc.identifier.urihttps://hdl.handle.net/20.500.12587/24267
dc.identifier.volume33
dc.identifier.wosWOS:001175381600004
dc.identifier.wosqualityQ4
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherAcad Sciences Czech Republic, Inst Computer Science
dc.relation.ispartofNeural Network World
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_20241229
dc.subjectbreast cancer; mammography; BI-RADS; convolutional neural network; support vector machines
dc.titleBI-RADS categories and breast lesions classification of mammographic images using artificial intelligence diagnostic model
dc.typeArticle

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