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dc.contributor.authorTasdemir, Sena Busra Yengec
dc.contributor.authorTasdemir, Kasim
dc.contributor.authorAydin, Zafer
dc.date.accessioned2021-05-24T09:01:46Z
dc.date.available2021-05-24T09:01:46Z
dc.date.issued2018en_US
dc.identifier.isbn978-1-5386-6805-4
dc.identifier.urihttps://doi.org/10.1109/ICMLA.2018.00023
dc.identifier.urihttps://hdl.handle.net/20.500.12573/739
dc.description.abstractDigital mammography is a widespread medical imaging technique that is used for early detection and diagnosis of breast cancer. Detecting the region of interest (ROI) helps to locate the abnormal areas, which may be analyzed further by a radiologist or a CAD system. In this paper, a new classification method is proposed for ROI detection in mammography images. Features are extracted using Wavelet transform, Haralick and HOG descriptors. To reduce the number of dimensions and eliminate irrelevant features, a wrapper-based feature selection method is implemented. Several feature extraction methods and machine learning classifiers are compared by performing a leave-one-image-out cross-validation experiment on a difficult dataset. The proposed feature extraction method provides the best accuracy of 87.5% and the second-best area under curve (AUC) score of 84% when employed in a random forest classifier.en_US
dc.description.sponsorshipIEEE; Assoc Machine Learning & Applicaten_US
dc.language.isoengen_US
dc.publisherIEEE, 345 E 47TH ST, NEW YORK, NY 10017 USAen_US
dc.relation.isversionof10.1109/ICMLA.2018.00023en_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectRandom Forest Classifieren_US
dc.subjectWavelet Decompositionen_US
dc.subjectHaralick Featuresen_US
dc.subjectROI detectionen_US
dc.titleROI Detection in Mammogram Images using Wavelet-Based Haralick and HOG Featuresen_US
dc.typeconferenceObjecten_US
dc.contributor.departmentAGÜ, Mühendislik Fakültesi, Mühendislik Bilimleri Bölümüen_US
dc.contributor.authorID0000-0003-4542-2728en_US
dc.identifier.startpage105en_US
dc.identifier.endpage109en_US
dc.relation.journal2018 17TH IEEE INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND APPLICATIONS (ICMLA)en_US
dc.relation.publicationcategoryKonferans Öğesi - Ulusal - Kurum Öğretim Elemanıen_US


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