Convolutional Neural Network for Classification of Histopathology Images for Breast Cancer Detection

One of the most common subtypes of all breast cancers is Invasive Ductal Carcinoma (IDC). Pathologists typically focus on regions which contain IDC to determine whether a patient suffers from breast cancer or not. We make use of publicly available Breast Histopathology Images dataset provided at the Kaggle for classification. In this dataset, images are delineated to extract the exact regions of IDC. This dataset contains 277,524 patches among which 198,738 belong to the negative class and 78,786 belong to the positive class (images with IDC). We present a novel deep convolutional neural network architecture for classification. Our performance in terms of area under the receiver operating characteristic curve for the detection of IDC is 0.94 on a set of 27,553 test images thereby setting a new benchmark for future research efforts.