IDC Breast Cancer Detection Using Deep Learning Schemes

Abstract

During the past few years, deep learning (DL) architectures are being employed in many potential areas such as object detection, face recognition, natural language processing, medical image analysis and other related applications. In these applications, DL has achieved remarkable results matching the performance of human experts. This paper presents a novel convolutional neural networks (CNN)-based approach for the detection of breast cancer in invasive ductal carcinoma tissue regions using whole slide images (WSI). It has been observed that breast cancer has been a leading cause of death among women. It also remains a striving task for pathologist to find the malignancy regions from WSI. In this research, we have implemented different CNN models which include VGG16, VGG19, Xception, Inception V3, MobileNetV2, ResNet50, and DenseNet. The experiments were performed on standard WSI slides data-set which include 163 patients of IDC. For performance evaluation, same data-set was divided into 113 and 49 images for training and testing, respectively. The testing was carried out separately over each model and the obtained results showed that our proposed CNN model achieved 83% accuracy which is better than the other models.