  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.
