Diagnosing breast cancer tumors using stacked ensemble model

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dc.authoridIkizceli, Turkan/0000-0002-5683-0391
dc.contributor.authorYurttakal, Ahmet Hasim
dc.contributor.authorErbay, Hasan
dc.contributor.authorIkizceli, Turkan
dc.contributor.authorKaracavus, Seyhan
dc.contributor.authorBicer, Cenker
dc.date.accessioned2025-01-21T16:37:43Z
dc.date.available2025-01-21T16:37:43Z
dc.date.issued2022
dc.departmentKırıkkale Üniversitesi
dc.description.abstractBreast cancer is the most common cancer that progresses from cells in the breast tissue among women. Early-stage detection could reduce death rates significantly, and the detection-stage determines the treatment process. Mammography is utilized to discover breast cancer at an early stage prior to any physical sign. However, mammography might return false-negative, in which case, if it is suspected that lesions might have cancer of chance greater than two percent, a biopsy is recommended. About 30 percent of biopsies result in malignancy that means the rate of unnecessary biopsies is high. So to reduce unnecessary biopsies, recently, due to its excellent capability in soft tissue imaging, Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) has been utilized to detect breast cancer. Nowadays, DCE-MRI is a highly recommended method not only to identify breast cancer but also to monitor its development, and to interpret tumorous regions. However, in addition to being a time-consuming process, the accuracy depends on radiologists' experience. Radiomic data, on the other hand, are used in medical imaging and have the potential to extract disease characteristics that can not be seen by the naked eye. Radiomics are hard-coded features and provide crucial information about the disease where it is imaged. Conversely, deep learning methods like convolutional neural networks(CNNs) learn features automatically from the dataset. Especially in medical imaging, CNNs' performance is better than compared to hard-coded features-based methods. However, combining the power of these two types of features increases accuracy significantly, which is especially critical in medicine. Herein, a stacked ensemble of gradient boosting and deep learning models were developed to classify breast tumors using DCE-MRI images. The model makes use of radiomics acquired from pixel information in breast DCE-MRI images. Prior to train the model, radiomics had been applied to the factor analysis to refine the feature set and eliminate unuseful features. The performance metrics, as well as the comparisons to some well-known machine learning methods, state the ensemble model outperforms its counterparts. The ensembled model's accuracy is 94.87% and its AUC value is 0.9728. The recall and precision are 1.0 and 0.9130, respectively, whereas F1-score is 0.9545.
dc.identifier.doi10.3233/JIFS-219176
dc.identifier.endpage85
dc.identifier.issn1064-1246
dc.identifier.issn1875-8967
dc.identifier.issue1
dc.identifier.scopus2-s2.0-85122835263
dc.identifier.scopusqualityQ1
dc.identifier.startpage77
dc.identifier.urihttps://doi.org/10.3233/JIFS-219176
dc.identifier.urihttps://hdl.handle.net/20.500.12587/24509
dc.identifier.volume42
dc.identifier.wosWOS:000741363900007
dc.identifier.wosqualityQ4
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherIos Press
dc.relation.ispartofJournal of Intelligent & Fuzzy Systems
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_20241229
dc.subjectStacked ensemble; radiomics; deep learning; gradient boosting; breast cancer
dc.titleDiagnosing breast cancer tumors using stacked ensemble model
dc.typeArticle

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