A Multi-channel Multi-classifier Method for Classifying Pancreatic Cystic Neoplasms Based on ResNet

摘要

Pancreatic cystic neoplasm (PCN) is one of the most common tumors in the digestive tract. It is still a challenging task for doctors to diagnose the types of pancreatic cystic neoplasms by using Computed Tomography (CT) images. Especially for serous cystic neoplasms (SCNs) and mucinous cystic neoplasms (MCNs), doctors hardly distinguish one from the other by the naked eyes owing to the high similarities between them. In this work, a multi-channel multiple-classifier (MCMC) model is proposed to distinguish the two pancreatic cystic neoplasms in CT images. At first, multi-channel images are used to enhance the image edge of the tumor, then the residual network is adopted to extract features. Finally, the multiple classifiers are applied to classify the results. Experiments show that the proposed method can effectively improve the classification effect, and the results can help doctors to utilize the CT images to achieve reliable non-invasive disease diagnosis.