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

TL;DR

This paper introduces a deep residual neural network approach for accurately diagnosing invasive ductal carcinoma in breast cancer histopathology images, achieving high accuracy and AUROC scores on a benchmark dataset.

Contribution

The study presents a novel deep residual convolutional network model that converts RGB images into seven-channel matrices for improved IDC classification in histopathology images.

Findings

Achieved 99.29% accuracy in IDC prediction

Attained an AUROC score of 0.9996

Demonstrated superior performance over existing methods

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 for about 80% of all breast cancers. According to the American Cancer Society, 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. The microscopic RGB images are converted into a seven channel image matrix, which is then fed to the network. The proposed model produces a 99.29% accurate approach towards the 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.