Computer-aided diagnosis system for breast cancer using RF classifier

Breast tumor is the most widely detected tumor and one of the major causes for malignancy demise in ladies around the world. The solution for this is early detection and diagnosis. Artificial Neural Network is used as emerging diagnostic tool for breast cancer. The objective of this research is to diagnose breast cancer with a machine learning method based on random forest classifier. MIAS database is used for the digital mammogram images. Preprocessing is generally needed to improve the low quality of image. ROI is determined according to size of suspicious area. After the suspicious region is segmented, features are extracted by texture analysis. Feature selection technique is used for the detection of High-dimensional features. A statistical method, gray-level co-occurrence matrix (GLCM) is used as a texture attribute to extract the suspicious area. From all extracted features best features are selected with the help of FCBF which is fast correlation-based feature selection technique. Selected features to improve the accuracy of classification are mean, standard deviation, smoothness, angular second moment (ASM), entropy, and correlation. Random Forest (RF) is used as a classifier. The results of present work show that the CAD system using RF classifier is very effective and achieves the best result in the diagnosis of breast cancer.