Enhanced Image Segmentation in Breast Cancer Classification Leveraging Deep learning

Abstract

In the recent years, the field of breast cancer research is using deep learning techniques to mitigate the problems of false positive and false negative cases caused by the breast cancer diagnosis done by the radiologist. Therefore, in this research we propose deep CNN (convolution neural networks) for classifying the mammogram image as cancerous or non-cancerous.  However, it is observed that, merely using deep learning techniques also has some limitations such as, uncertainty in breast mass classification on the dense breast mammograms. Thus, in this research the images are preprocessed and segmented prior classification. In this research images are preprocessed using Rolling Ball algorithm for background removal and then compared CLAHE and Unsharp masking for improving image contrast and visibility of the images. The proposed work segmented MIAS preprocessed images using k-means algorithm. These enhanced images are provided to Deep CNN (Convolution Neural Networks) and its features extracted for classifying images as benign, malignant or normal. To improve the CNN's structure and lessen overfitting, dropout and zero-padding are employed. The proposed work is tested on mammography images from the databases of the Mammographic Image Analysis Society (mini-MIAS), Breast Cancer Digital Repository (BCDR). The proposed system has, Accuracy:0.89%, Precision: 0.91%, Recall: 0.90%, F1-score 0.88% and for the MIAS dataset, respectively.