基于深度学习的胰腺黏液性和浆液性囊性肿瘤的多源特征分类模型

徐真顺,袁小涵,黄子珩,邵成伟,武杰,边云

Multi-source Feature Classification Model of Pancreatic Mucinous and Serous Cystic Neoplasms Based on Deep Learning

XU Zhenshun,YUAN Xiaohan,HUANG Ziheng,SHAO Chengwei,WU Jie,BIAN Yun

表3 特征模型在4种分类器中的效能

Table 3 Performance of the feature models in the four classifiers

特征模型 分类器 准确率 召回率 精确率 AUC F1 RAD SVM 0.8077 0.5789 0.8462 0.7592 0.6875 ADAboost 0.8269 0.6842 0.8125 0.7967 0.7429 Random Forest 0.7885 0.6742 0.7222 0.7663 0.6974 Logistic 0.8269 0.6316 0.8571 0.7855 0.7273 DL SVM 0.6731 0.1176 0.5303 0.5595 0.1925 ADAboost 0.7692 0.9000 0.6429 0.8645 0.7500 Random Forest 0.7115 0.7619 0.6154 0.8407 0.6809 Logistic 0.7500 0.2857 0.5714 0.5770 0.3809 RAD_DL SVM 0.8462 0.6111 0.7908 0.8051 0.6894 ADAboost 0.8269 0.7727 0.8095 0.6882 0.7907 Random Forest 0.8077 0.6364 0.8750 0.8051 0.7369 Logistic 0.8462 0.9444 0.7083 0.8441 0.8095 Clinical_RAD_DL SVM 0.8846 0.8235 0.8235 0.8689 0.8235 ADAboost 0.8654 0.8823 0.7500 0.8697 0.8108 Random Forest 0.7692 0.7059 0.6316 0.7529 0.6667 Logistic 0.9231 0.8824 0.8820 0.9126 0.8822