Detection of mitotic cells in breast cancer histopathological images using deep versus handcrafted features

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dc.authorid0000-0002-0738-871Xen_US
dc.contributor.authorSığırcı, İ. Onur
dc.contributor.authorAlbayrak, Abdülkadir
dc.contributor.authorBilgin, Gökhan
dc.date.accessioned2022-05-31T06:51:00Z
dc.date.available2022-05-31T06:51:00Z
dc.date.issued2022en_US
dc.departmentDicle Üniversitesi, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümüen_US
dc.descriptionWOS:000618948700006
dc.description.abstractOne of the most important processes in the diagnosis of breast cancer, which is the leading mortality rate in women, is the detection of the mitosis stage at the cellular level. In literature, many studies have been proposed on the computer-aided diagnosis (CAD) system for detecting mitotic cells in breast cancer histopathological images. In this study, comparative evaluation of conventional and deep learning based feature extraction methods for automatic detection of mitosis in histopathological images are focused. While various handcrafted features are extracted with textural/spatial, statistical and shape-based methods in conventional approach, the convolutional neural network structure proposed on the deep learning approach aims to create an architecture that extracts the features of small cellular structures such as mitotic cells. Mitosis detection/counting is an important process that helps us assess how aggressive or malignant the cancer's spread is. In the proposed study, approximately 180,000 non-mitotic and 748 mitotic cells are extracted for the evaluations. It is obvious that the classification stage cannot be performed properly due to the imbalanced numbers of mitotic and non-mitotic cells extracted from histopathological images. Hence, the random under-sampling boosting (RUSBoost) method is exploited to overcome this problem. The proposed framework is tested on mitosis detection in breast cancer histopathological images dataset provided from the International Conference on Pattern Recognition (ICPR) 2014 contest. In the results obtained with the deep learning approach, 79.42% recall, 96.78% precision and 86.97% F-measure values are achieved more successfully than handcrafted methods. A client/server-based framework has also been developed as a secondary decision support system for use by pathologists in hospitals. Thus, it is aimed that pathologists will be able to detect mitotic cells in various histopathological images more easily through necessary interfaces.en_US
dc.identifier.citationSığırcı, İ.O., Albayrak, A. ve Bilgin, G. (2022). Detection of mitotic cells in breast cancer histopathological images using deep versus handcrafted features. Multimedia Tools and Applications, 81(10), 13179-13202.en_US
dc.identifier.doi10.1007/s11042-021-10539-2
dc.identifier.endpage13202en_US
dc.identifier.issn1380-7501
dc.identifier.issn1573-7721
dc.identifier.issue10en_US
dc.identifier.scopus2-s2.0-85101149731
dc.identifier.scopusqualityQ1
dc.identifier.startpage13179en_US
dc.identifier.urihttps://link.springer.com/content/pdf/10.1007/s11042-021-10539-2.pdf
dc.identifier.urihttps://hdl.handle.net/11468/9931
dc.identifier.volume81en_US
dc.identifier.wosWOS:000618948700006
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthorAlbayrak, Abdülkadir
dc.language.isoenen_US
dc.publisherSpringeren_US
dc.relation.ispartofMultimedia Tools and Applications
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectDigital histopathologyen_US
dc.subjectMitosis detectionen_US
dc.subjectDeep learningen_US
dc.subjectConvolutional neural networksen_US
dc.subjectImage spatial statisticsen_US
dc.subjectImbalanced dataen_US
dc.titleDetection of mitotic cells in breast cancer histopathological images using deep versus handcrafted featuresen_US
dc.titleDetection of mitotic cells in breast cancer histopathological images using deep versus handcrafted features
dc.typeArticleen_US

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