DCT FEATURES BASED MALIGNANCY AND ABNORMALITY TYPE DETECTION METHOD FOR MAMMOGRAMS

Radiologists are interested in finding the stage of cancer, so the patient can be treated and cured accordingly. This is possible by finding the type of abnormality to measure the severity of cancer in mammograms. CAD could provide them the option 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 classifiers to identify malignant mammograms and the results of winning algorithm that is Support Vector Machine (SVM) in this case are considered for next processing. Mammograms declared as malignant by SVM are divided into six classes. Again, binary classifier (SVM) is used for multi-classification using one against all technique for classification. Output of all classifiers is combined by max, median and mean rule. It has been noted that results are very much satisfactory and accuracy of classification of abnormalities is more than 96% in case of max rule. MIAS [47] data set is used for experimentation purpose.