A Computer-Aided Detection System for Breast Cancer Detection and Classification

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dc.authorid0000-0001-9200-5535en_US
dc.authorid0000-0003-2336-7924en_US
dc.contributor.authorFadhil, Abdullah Freidoon
dc.contributor.authorOrnek, Humar Kahramanli
dc.date.accessioned2023-01-29T16:36:16Z
dc.date.available2023-01-29T16:36:16Z
dc.date.issued2021en_US
dc.departmentSelçuk Üniversitesi, Teknoloji Fakültesi, Bilgisayar Mühendisliği Bölümüen_US
dc.description.abstractBreast cancer is a dangerous disease and considered the second cause of death for women globally. Reading breast cancer images requires experienced radiologists. Radiologists may have a problem with their visual decision about breast cancer. Therefore, a computer-aided detection (CAD) system is needed to help radiologists in their decisions. The early detection of breast cancer using computer vision systems, such as image processing, increases the success of treatment. Developing a well-designed CAD system is still a challenging problem because of the low rate of accuracy performance. In this paper, an improved CAD system is introduced for classifying breast cancer tumors into normal and abnormal classes. In this CAD system, a region-based segmentation approach, namely region growing, is used. Discrete wavelet transform is used for the histogram and texture-based feature extraction. The 120 candidate features were ranked and selected according to two criteria which are the interclass separation and classification accuracy criterion. Four different classifiers, Linear Discriminant Analysis, Artificial Neural Network, Decision Tree, and Support Vector Machine, were used for classification. The results are obtained using a 10-Fold cross-validation technique on the MIAS data set. The highest accuracy achieved was 93.6% by Support Vector Machine classifier using the best 69 features from the interclass separation method. The sensitivity and specificity achieved were 89.2% and 99.0%, respectively. The results show improved accuracy compared to previous works selected from the literature review.en_US
dc.identifier.citationFadhil, A. F., Ornek, H. K., (2021). A Computer-Aided Detection System for Breast Cancer Detection and Classification. Selcuk University Journal of Engineering Sciences, 20 (01), 23-31.en_US
dc.identifier.endpage31en_US
dc.identifier.issn2757-8828en_US
dc.identifier.issue1en_US
dc.identifier.startpage23en_US
dc.identifier.urihttps://hdl.handle.net/20.500.12395/45059
dc.identifier.volume20en_US
dc.institutionauthorFadhil, Abdullah Freidoon
dc.institutionauthorOrnek, Humar Kahramanli
dc.language.isoenen_US
dc.publisherSelçuk Üniversitesien_US
dc.relation.ispartofSelcuk University Journal of Engineering Sciencesen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.selcuk20240510_oaigen_US
dc.subjectArtificial Neural Networken_US
dc.subjectBreast Cancer Detectionen_US
dc.subjectDecision Treeen_US
dc.subjectDiscrete Wavelet Transformen_US
dc.subjectLinear Discriminant Analysisen_US
dc.subjectSupport Vector Machineen_US
dc.titleA Computer-Aided Detection System for Breast Cancer Detection and Classificationen_US
dc.typeArticleen_US

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