Predicting Invasive Ductal Carcinoma in breast histology images using Convolutional Neural Network

Abstract

Over the past ten years, there has been a rise in using deep learning for medical image analysis such as CNN. Deep learning is used extensively in the field of healthcare to identify patterns, classify and segment tumors and so on. The classification of breast cancer is a well-known problem that attracts the attention of many researchers in the field of healthcare because breast cancer is the second major cause of cancer-related deaths in women. The most common subtype of all breast cancers is the Invasive Ductal Carcinoma (IDC). There are many ways to identify this type of breast cancer such as a biopsy where tissue is removed from patient and studied under microscope. The biopsy is followed by a diagnosis which is based on the qualification of the pathologists, who will look for abnormal cells. The next task for pathologists is to assign an aggressiveness grade to a whole mount sample. To do this, pathologists focus on the region of interest which contain the IDC. Therefore, one of the popular pre-processing steps for automatic aggressiveness grading is to delineate the exact regions of IDC inside of a whole mount slide. In this paper, we have experimentally tested two CNN models using depthwise separable convolution and standard convolution to enhance the accuracy of the convolutional neural network. We tested different types of activation functions such as ReLU, Sigmoid, and Tanh. As well as applying gaussian noise to test the robustness of the two models. The results show convolutional neural networks outperformed the softmax classifier, with standard convolution and ReLU where we achieved ~87.5% classification accuracy, ~93.5% sensitivity, and ~71.5% specificity.