Classification of Breast Cancer from Mammogram images using Deep Convolution Neural Networks

Abstract

Breast cancer is intrusive form of cancer which affects every 1 woman out of 9 in Pakistan. To detect breast cancer at early stage, mammography technique is used which is a manual process and is susceptible to radiologist error. Therefore, this paper proposes a new CAD technique, which relies on customized deep convolutional neural network to detect and classify breast cancer into malignant and benign. Mammogram images from digital database for screening mammography dataset are used to train proposed model. First, region of interest is extracted using region based segmentation technique which is further enhanced using contrast limited adaptive histogram equalization. Later, a customized deep convolution neural network is used to learn features from mammograms. Support vector machine classifier is used to classify breast masses into benign and malignant. 88.7% accuracy is achieved with 0.885 area under the curve. Other parameters like System specificity, sensitivity, precision, F1 score and AUC are recorded as 0.93, 0.841, 0.917, 0.877 and 0.885 respectively.