A deep convolutional neural network architecture for breast mass classification using mammogram images

Abstract

Breast cancer is one of the second most common cancer occurring worldwide. Early identification of the disease is a major interest that promises to propose several diagnostic procedures to prevent further surgical interventions. This research paper aims to develop a breast mass classifier system using deep learning to differentiate breast mass images from normal mammographic images. The benchmark mammographic datasets CBIS-DDSM, INbreast, and mini-MIAS are used for constructing the proposed model DELU-BM-CNN. The region of interest is identified by applying image processing techniques (median filter, binarization and dilation) and the images are enhanced and sharpened using adaptive histogram equalization and unsharp masking techniques. The pre-processed images are trained with a minimum of five deep convolutional layers activated by an Exponential Linear Unit (ELU) which is developed from scratch for feature learning and classifying the given whole mammographic images. Dropout, Data normalization, and Global average pooling are some of the regularization techniques adopted to prevent the model from over-fitting. The proposed models are able to classify CBIS-DDSM images with an accuracy of 96.60%, INbreast images with 96.20% and MIAS images with 97.40%. The experimental results are also compared with conventional Rectified Linear Unit (ReLU) and Leaky ReLU activation function that promises the proposed model as a good prognosticator than the state-of-art models for cancer diagnosis using mammogram images as input.