Automatic System for Detecting Invasive Ductal Carcinoma Using Convolutional Neural Networks

Invasive ductal carcinoma (IDC) is the most common type of breast cancer. Every year a numerous number of women in this world are diagnosed as having IDC. Accurately detecting IDC is a time consuming and challenging task as the pathologists need to focus on the specific regions of whole slide images (WSI) that contain IDC. Precise and early diagnosis of IDC is a must because it helps to estimate the subsequent tumor aggressiveness that can be caused by this type of breast cancer. The goal of this research is to create an automated system that will analyze the whole mount slide images of breast cancer specimens to indicate the exact positions of IDC inside of the slides and give a decision based on the results. A multilayered convolutional neural network is designed which is trained over a large number of whole slide images. The dataset consists of 162 cases of patients diagnosed with IDC. We found an accuracy of 89.34% in f1 score using convolutional neural network to achieve the state of the art result on IDC classification.