Mlo and CC view of feature fusion and mammogram classification using deep convolution neural network

Abstract

Breast cancer is the most frequent type of cancer in women all over the world. The improvement of computer aided system help the radiologist for the effective analysis and diagnosis of breast cancer. It presents a computational methodology for classifying breast cancer as normal, benign and malignant from CC and MLO views of mammogram image. The proposed strategy consists of feature extraction, multiple view feature fusion and classification. The input images are fed into feature extraction where convolution neural network is applied. The CNN is nicely suitable for both feature extraction, feature fusion and mammogram classification. In this framework, convolution layer, pooling and activation function are used as a feature extraction techniques. After the process of feature extraction, feature fusion is employed by average pooling of CNN. The feature fusion will increase or maximize the relevant information of the breast image. Finally obtained features from the fusion are fed into CNN classifier in which softmax and fully connected layer are employed as a classifier techniques. The proposed work achieves 98.4% of accuracy to classify the breast cancer from MLO and CC views using hybrid feature with CNN classifier.