Classification of Pancreatic Cystic Tumors Based on DenseNet and Transfer Learning

Abstract

This work applied the classification model of DenseNet combined with transfer learning to classify mucinous cystic tumor (MCN) from serous cystic tumor (SCN) of the pancreas. Firstly, the data of 65 MCNs and 107 SCNs from Changhai Hospital were augemented and preprocessed. Secondly, the classification model of DenseNet combined with transfer learning was constructed and fine-tuned, MCN and SCN were classified by 5-fold cross validation, and the proposed classification model was compared with AlexNet, VGG16, ResNet50 and other deep learning models. The classification model in this paper yielded the best recognition effect. The area under the ROC curve (AUC value), accuracy rate, recall rate and precision rate of the test set were 0.989, 0.943, 0.949 and 0.938 respectively. It proved that the classification model based on DenseNet combined with transfer learning has higher recognition accuracy for MCN and SCN and stronger learning ability than other deep learning models, which can help doctors in clinical diagnosis, and save manpower and material resources. It further confirmed the potential value and clinical significance of this model for the classification of pancreatic cystic tumors.

Keywords

• deep learning
• densenet
• transfer learning
• cystic tumor of pancreas