A Novel method for IDC Prediction in Breast Cancer Histopathology images using Deep Residual Neural Networks

Abstract

Invasive ductal carcinoma (IDC), which is also sometimes known as the infiltrating ductal carcinoma, is the most regular form of breast cancer. It accounts to about 80% of all breast cancers. According to American Cancer Society [1], more than 180, 000 women in the United States are diagnosed with invasive breast cancer each year. The survival rate associated with this form of cancer is about 77% to 93% depending on the stage at which they are being diagnosed. The invasiveness and the frequency of the occurrence of these disease makes it one of the difficult cancers to be diagnosed. Our proposed methodology involves diagnosing the invasive ductal carcinoma with a deep residual convolution network to classify the IDC affected histopathological images from the normal images. The dataset for the purpose used is a benchmark dataset known as the Breast Histopathology Images [2]. The microscopic RGB images are converted into a seven channel image matrix, which are then fed to the network. The proposed model produces a 99.29% accurate approach towards prediction of IDC in the histopathology images with an AUROC score of 0.9996. Classification ability of the model is tested using standard performance metrics. The following methodology has been described in the next sections.

A Comprehensive Review of Computer-Aided Models for Breast Cancer Diagnosis Using Histopathology Images

“…Digital filters are designed to reduce or remove noise and artifacts in an image [54][55][56]. Hirra et al classified histopathological images using patch-based deep-learning modeling.…”

“…A study out of Jalpaiguri Government Engineering College in India used a deep residual neural network to detect breast cancer in histopathology images. The Gaussian blur algorithm was used for the denoising of images with low resolutions to reduce regions specifically affected by the noise [56].…”