Identification of malignant masses on digital mammogram images based on texture feature and correlation based feature selection

Abstract

The most popular techniques in early breast cancer detection is using digital mammogram. However, the challenge lies in early and accurate detection the irregular masses with spiculated margin as the most common abnormality. This paper proposes an image classifier to classify the mammogram images. The abnormality that can be founded in mammogram image is classified into malignant, benign and normal cases. By applying Computer Aided Diagnosis (CAD), totally 12 features comprising of histogram and GLCM as the texture based features are extracted from the mammogram image. Correlation based feature selection (CFS) is used in this paper which reduces 50% of the features. Multilayer perceptron algorithm is applied to mammography classification by using these selected features. The experimental result shows that 40 digital mammograms data taken from private Oncology Clinic Kotabaru Yogyakarta was achieved 91.66% of accuracy. The approach can be beneficial to radiologists for more accurate diagnosis.