DCT FEATURES BASED MALIGNANCY AND ABNORMALITY TYPE DETECTION METHOD FOR MAMMOGRAMS
M. Arfan Jaffar, Nawazish Naveed, Sultan Zia, Bilal Ahmed, Tae‐Sun Choi · 2011
Radiologists are interested innding the stage of cancer, so the patient can be treated and cured accordingly. This is possible bynding the type of abnormality to measure the severity of cancer in mammograms. CAD could provide them the op- tion of better opinion about the type of abnormality. In this paper, we have proposed a novel method which can classify cancerous mammogram into six classes. Features are extracted from preprocessed images and passed through different classiers to identify malignant mammograms and the results of winning algorithm that is Support Vector Ma- chine (SVM) in this case are considered for next processing. Mammograms declared as malignant by SVM are divided into six classes. Again, binary classier (SVM) is used for multi-classicat using one against all technique for classication. Output of all classiers is combined by max, median and mean rule. It has been noted that results are very much satisfactory and accuracy of classication of abnormalities is more than 96% in case of max rule. MIAS (47) data set is used for experimentation purpose.