Analysis of breast cancer classification robustness with radiomics feature extraction and deep learning techniques

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dc.authoridCevik, Ulus/0000-0002-0956-9725
dc.authoridIBRIKCI, Turgay/0000-0003-1321-2523
dc.authoridPaydas, Semra/0000-0003-4642-3693
dc.authoridRashid, Harun Ur/0000-0003-0874-7590
dc.contributor.authorRashid, Harun Ur
dc.contributor.authorIbrikci, Turgay
dc.contributor.authorPaydas, Semra
dc.contributor.authorBinokay, Figen
dc.contributor.authorCevik, Ulus
dc.date.accessioned2025-01-06T17:36:45Z
dc.date.available2025-01-06T17:36:45Z
dc.date.issued2022
dc.description.abstractBreast cancer and breast imaging diagnostic procedures are typically carried out using a variety of imaging modalities, including mammography, MRI, and ultrasound. However, ultrasound and mammography have limitations and MRI is recognized as better than other procedures. Recent computational approaches, such as radiomics, applied to image analysis have shown remarkable progress in lowering diagnostic difficulties. This research analysed the robustness of breast tumour classification with feature extraction (radiomics) and a featureless method (deep learning). The proposal consists of two stages: the first stage introduced and explored radiomics-based steps. A total of 111 tumour lesions were used to derive 74 radiomic features consisting of shape, and three separate second-order metrics. Associations of these features were used to classify tumour lesions with four different kernels from support vector machine algorithm. In the confusion matrix analysis, the SVM-RBF kernel developed optimal diagnostic efficiency with a maximum test accuracy of 97.06% on the combination of feature analysis. The second stage developed with deep learning techniques (InceptionV3 and CNN-SVM). A total of 2998 images were used to create the models. In this portion, the CNN-SVM model achieved the highest accuracy, 95.28%, with an AUC of 0.974, where the pre-trained InceptionV3 achieved an AUC of only 0.932. Finally, the obtained result in both stages was discussed together and other related studies.
dc.identifier.doi10.1111/exsy.13018
dc.identifier.issn0266-4720
dc.identifier.issn1468-0394
dc.identifier.issue8
dc.identifier.scopus2-s2.0-85128962545
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.1111/exsy.13018
dc.identifier.urihttps://hdl.handle.net/20.500.14669/1989
dc.identifier.volume39
dc.identifier.wosWOS:000788266600001
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherWiley
dc.relation.ispartofExpert Systems
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_20241211
dc.subjectbreast tumour classification
dc.subjectdeep learning
dc.subjectradiomic features
dc.titleAnalysis of breast cancer classification robustness with radiomics feature extraction and deep learning techniques
dc.typeArticle

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