Analysis of digital mammograms for detection of breast cancer

Digital mammogram has become the most effective technique for early breast cancer detection. The most common abnormality that may indicate breast cancer is masses. The challenge lies in early and accurate detection to overcome the development of breast cancer that affects more and more women throughout the world. 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 and feature selection method in order to build a CAD to discriminate normal, benign, and malignant. Preprocessing needs to enhance the poor quality of image and remove the artifact caused by preprocessing step. 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 according to their class each other. 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 best feature that guarantee the improvement of classification with less feature dimension. The result shows that the proposed method was achieved the accuracy 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.