ANALYSIS OF DIGITAL MAMMOGRAMS FOR DETECTION OF BREAST CANCER
ABSTRACT: Mammogram is One of the most popular techniques for early detection of breast cancer. Masses in the most abnormality as the first signs of the breast cancer. Early detection is expected to overcome the mortality of the women and can be reduce the risk factor for women whose undetected breast cancer yet. Computer Aided Diagnosis (CAD) is used to help the radiologist in interpretation and recognition the pattern of the mammogram abnormality. The main objective of this research is to perform and analyze the contrast enhancement, feature extraction and feature selection method in order to build a CAD to recognize the types of normal, benign, and malignant cases. Preprocessing needs to enhance the poor quality of image and remove the artifact caused by preprocessing step. Region of Interest (ROI) as the suspicious area segmented, and then extracted by texture feature approach. High dimensionality of feature is selected by feature selection technique and would be classified and recognized according to their class. The digital mammogram images are taken from the Private database of Oncology Clinic Kotabaru Yogyakarta. The dataset consists of 40 mammogram images with 14 benign cases, 6 malignant cases, and 20 normal cases. The proposed method in preprocessing step made the image enhanced and proved by MSE and PSNR value. Histogram and gray level co-occurrence matrix (GLCM) as the texture feature are used to extract the suspicious area. Correlation based feature selection (CFS) is used to select the best feature among 12 extracted features before. Mean, standard deviation, smoothness, angular second moment (ASM), entropy, and correlation are the optimal features that guarantee the improvement of accuracy with fewer features dimension. The result shows that the proposed method was achieved the accuracy about 96.66%, sensitivity 96.73%, specificity 97.35%, and ROC 96.6%. It is expected to contribute for helping the radiologist as material consideration in decision-making. Keywords: Breast cancer, Mammogram, Masses, CAD, Texture Feature, Feature selection, Correlation based feature selection.
