Automatic Detection of Invasive Ductal Carcinoma Based on the Fusion of Multi-Scale Residual Convolutional Neural Network and SVM
Jianfei Zhang, Xiaoyan Guo, Bo Wang, Wensheng Cui
Abstract
Invasive ductal carcinoma (IDC) is the most common type of breast cancer which is the leading cause of cancer-related deaths in middle-aged women. Pathological analysis of biopsy is the gold standard for diagnosis of breast cancer, and early detection, diagnosis, and treatment can significantly increase the survival rate. This paper proposes a method for the automatic detection of IDC based on the fusion of multi-scale residual convolutional neural network (MSRCNN) and SVM. First, the patches from whole slide images (WSI) were preprocessed by data enhancement and normalization and then input into the MSRCNN for features extraction. Second, the extracted features were input to the SVM and are classified into two categories: healthy and diseased patches. Finally, it is restored to the WSI according to the coordinate information of the patches, therefore the IDC and healthy tissue regions were built. Experimental results show that after 5-fold cross-validation, our method obtained an average accuracy of 87.45±0.81%, an average balance accuracy of 85.7±0.95%, and an average F1 score of 79.89±1.11%. Consequently, it has important practical value and scientific research significance.