Master Thesis

Maurice Samulski

Master ThesisBreast cancer is the most common life-threatening type of cancer affecting women in The Netherlands. The success of the treatment of breast cancer largely depends on the stage of a tumor at the time of detection. Therefore, early detection of breast cancer is essential. Although mammography screening is currently the most effective tool for early detection of breast cancer, up to one-fifth of women with invasive breast cancer have a mammogram that is interpreted as normal, i.e., a false-negative mammogram result. To overcome such limitations, Computer-Aided Diagnosis (CAD) systems for automatic classification of breast lesions as either benign or malignant are being developed. CAD systems help radiologists with the interpretation of lesions, such that they refer less women for further examination when they actually have benign lesions. In this thesis we constructed several types of classifiers, i.e., Bayesian networks and support vector machines, for the task of computer-aided diagnosis of breast lesions.

Classification of Breast Lesions in Digital Mammograms, Master Thesis (1.3 MB PDF)

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