A Novel Screening Framework for Lymph Node Metastasis in Colorectal Cancer Based on Deep Learning Approaches

As a diagnostic criterion for cancer, histopathology image analysis is quite critical for the subsequent therapeutic treatment of patients. Nowadays, the diagnosis is mainly depended on manually which is less precise and low-accuracy. To address the problem, we propose a novel screening framework combined image preprocess and AI approaches for the automatic detection of lymph node metastasis of colorectal cancer. First calculates the Histogram of Oriented Gradient (HOG) and Gray Level Cooccurrence Matrix (GLCM) of high-resolution digital images transformed from pathological sections. Statistical analysis show that Support Vector Machine (SVM) can be used to automatically identify cancerous areas. We further introduce deep learning models Convolutional Neural Network (CNN) into our framework, taking preprocessed images as inputs. The screening results demonstrate that the highest overlapping ratio can be achieved compared with manually annotation areas is 93.09% got by CNN, while another approaches SVM get an accuracy of 83.75%. The combination of image preprocess and deep learning can effectively improve the efficiency of lymph node metastasis screening in colorectal cancer and has great significance for the further development of Computer Aided Diagnosis (CAD) systems.