Application of data mining algorithms for mammogram classification

One of the leading causes of cancer death among women is breast cancer. In our work we aim at proposing a prototype of a medical expert system (based on data mining techniques) that could significantly aid medical experts to detect breast cancer. This paper presents the CAD (computer aided diagnosis) system for the detection of normal and abnormal pattern in the breast. The proposed system consists of four major steps: the image preprocessing, the feature extraction, the feature selection and the classification process that classifies mammogram into normal (without tumor) and abnormal (with tumor) pattern. After removing noise from mammogram using the Discrete Wavelet Transformation (DWT), first is selected the region of interest (ROI). By identifying the boundary of the breast, it is possible to remove any artifact present outside the breast area, such as patient markings. Then, a total of 20 GLCM features are extracted from the ROI, which were used as inputs for classification algorithms. In order to compare the classification results, we used seven different classifiers. Normal breast images and breast image with masses (total 322 images) used as input images in this study are taken from the mini-MIAS database.