Mammogram Analysis Using Feed-Forward Back Propagation and Cascade-Forward Back Propagation Artificial Neural Network

Breast cancer is one of the leading causes of cancer deaths among women in developed countries including India. Mammography is currently the most effective method for detection of breast cancer. Early diagnosis of the breast cancer allows treatment which could lead to high survival rate. This paper presents breast cancer detection in digital mammography using Image Processing Techniques by Artificial Neural Networks. A clinical database of 42 previously verified patient cases are employed and randomly partitioned into two independent sets for training and testing. Gray Level Co-occurrence Matrix (GLCM) features extracted from the known Mammogram images are used to train Artificial Neural Network based detection system. In Testing/Recognition Phase the extracted features of known and unknown Mammogram images are compared for classification of images into malignant and benign. Feed-forward back propagation and Cascade-forward back propagation Artificial Neural Network structures had been trained for detection. The performance is evaluated on the basis of Mean Square Error (MSE) and accuracy of both the structure has been compared.