Classification of Tissue Types in Histological Colorectal Cancer Images Using Residual Networks
Ko-Hua Lai, Meng‐Chou Chang · 2019
Colorectal cancer (CRC) is one of the most prevalent cancer types, but until now only few studies have addressed the problem of automatic recognition of distinct tissue types in histological CRC images. In this paper, we employ two deep convolutional neural networks, the original 50-layer residual network ResNet-50 and a modified residual network ResNet-50-fla-drop, to automatically perform eight-class tissue separation in histological CRC images. Experimental results showed that the proposed modified residual network ResNet-50-fla-drop can achieve a classification accuracy of 94.4% for eight-class CRC tissue separation, and it outperforms other published machine learning methods, including the radial-basis function SVM and VGG-VD-16.