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dc.contributor.authorTAŞDEMİR, SENA BÜŞRA YENGEÇ
dc.identifier.otherTez No: 541544
dc.description.abstractAmong females, leading cause of cancer death and the most common cancer type is breast cancer. Early detection is vital because it reduces the mortality rate. Digital mammography is a widespread medical imaging technique that is used for early detection and diagnosis of the breast cancer. Automatic detection of tumorous area from the digital mammography image helps to locate the abnormal tissues, which may be analyzed further by a radiologist. It has two main stages: feature extraction and classification. In this work, numerous feature extraction methods have been tested such as 2D-DWT, HOG, Haralick’s textural features, TAS, LBP, Zernike and GLCM. In order to select the most suitable classifier, the following classifiers also have been tested: random forest, logistic regression, k-nearest neighbors, naïve Bayes, decision tree, support vector machines, Adaboost, radial basis function network, multilayer perceptron, convolutional neural network. Based on comprehensive experiments, the optimum combination of feature extraction, feature selection and classification methods are identified. The proposed method, which employs CLAHE as image pre-processing tool, 2D-DWT, HOG, Haralick as feature extraction methods, wrapper as the feature selection method and random forest as the classifier, attained an accuracy of 87.5%en_US
dc.publisherAbdullah Gül Üniversitesien_US
dc.subjectBreast Canceren_US
dc.subjectROI detectionen_US
dc.subjectHaralick Featuresen_US
dc.subjectWavelet Decompositionen_US
dc.subjectHOG Featuresen_US
dc.subjectRandom Forest Classifieren_US
dc.titleEarly prognosis of breast cancer using image processing and machine learningen_US
dc.title.alternativeGörüntü işleme ve makine öğrenmesi yöntemiyle erken meme kanseri teşhisien_US
dc.contributor.departmentAGÜ, Fen Bilimleri Enstitüsü, Elektrik ve Bilgisayar Mühendisliği Ana Bilim Dalıen_US
dc.contributor.institutionauthorTAŞDEMİR, SENA BÜŞRA YENGEÇ

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