Classification of Breast Cancer Histopathological Images using Convolutional Neural Networks

Abstract

Breast Cancer is one of the mostly encountered cancers among women which involve the age group of 60-80 years mostly. The traditional methodology involves use of mammogram scan followed by various other clinical tests for assuring cancer prevailing in the body manually, which involves mistakes and delay in detection. Many times, it is detected using the biopsy method where tissue removed from the breast is studied under a microscope. This entire process is done by the histopathologies, and if he is not well trained, it may lead to wrong diagnosis. In order to improve the diagnosis by proper detection, automatic analysis of histopathology images has helped the pathologists in efficient diagnosis. Recently the Convolutional neural networks (CNN) have become a preferred deep learning method for breast cancer classification. In this paper, we have proposed CNN architecture based on Local Binary Pattern (LBP) images as input and then compare their classification results by a standard CNN based on origin images as input. Here, classification approach is proposed for automatic classification into either moderate stage or mild stage of cancer. An image dataset of 100 images is used in this approach and 80% of the dataset is used for training and the rest 20% of the images used for testing. 100% classification accuracy is obtained with CNN architecture. The results are compared with various state-of-art machine learning models.