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山东大学学报 (工学版) ›› 2019, Vol. 49 ›› Issue (2): 74-79.doi: 10.6040/j.issn.1672-3961.0.2018.273

• 机器学习与数据挖掘 • 上一篇    下一篇

基于AlexNet和集成分类器的乳腺癌计算机辅助诊断方法

侯霄雄1,2(),许新征1,2,*(),朱炯1,郭燕燕1   

  1. 1. 中国矿业大学计算机科学与技术学院, 江苏 徐州 221116
    2. 广西高校复杂系统与智能计算重点实验室, 广西 南宁 530006
  • 收稿日期:2018-07-06 出版日期:2019-04-20 发布日期:2019-04-19
  • 通讯作者: 许新征 E-mail:961458517@qq.com;xuxinzh@163.com
  • 作者简介:侯霄雄(1991—),男,山西曲沃人,硕士研究生,主要研究方向为机器学习,医学图像处理等. E-mail:961458517@qq.com
  • 基金资助:
    国家自然科学基金项目(61672522);广西高校复杂系统与智能计算重点实验室开放课题重点项目(2017CSCI01)

Computer aided diagnosis method for breast cancer based on AlexNet and ensemble classifiers

Xiaoxiong HOU1,2(),Xinzheng XU1,2,*(),Jiong ZHU1,Yanyan GUO1   

  1. 1. School of Computer Science and Technology, China University of Mining and Technology, Xuzhou 221116, Jiangsu, China
    2. Guangxi High School Key Laboratory of Complex System and Computational Intelligence, Nanning 530006, Guangxi, China
  • Received:2018-07-06 Online:2019-04-20 Published:2019-04-19
  • Contact: Xinzheng XU E-mail:961458517@qq.com;xuxinzh@163.com
  • Supported by:
    国家自然科学基金项目(61672522);广西高校复杂系统与智能计算重点实验室开放课题重点项目(2017CSCI01)

摘要:

为解决在计算机辅助诊断(computer aided diagnosis, CAD)中采用人工提取医学影像特征的弊端,在ImageNet数据集上预训练深度神经网络模型Alexnet,通过迁移学习再训练后的Alexnet模型对医学影像进行特征提取,利用集成学习方法训练分类器进行分类。试验结果表明,基于Alexnet和随机森林方法的分类器正确率达到了0.87±0.03,集成分类器的分类性能优于单一分类器。

关键词: 医学影像分析, 深度学习, 卷积神经网络, 计算机辅助诊断, 集成分类器

Abstract:

In order to solve the manual feature extraction of medical images in computer aided diagnosis, Alexnet was pre-trained on the ImageNet dataset, and feature extraction was performed on the medical image based on Alexnet with transfer learning. The ensemble learning method was used to train the classifier to classify and obtain a better classification effect than the single classifier. The results showed that the AUC(area under curve) of Alexnet deep learning model and random forest ensemble classifier reached 0.87±0.03, and the effect of the ensemble classifier was better than that of the single classifier in the same network depth.

Key words: medical image analysis, deep learning, convolutional neural network, computer aided diagnosis, ensemble classifiers

中图分类号: 

  • TP391

图1

计算机辅助诊断工作流图"

图2

预处理执行步骤"

图3

Alexnet网络结构"

表1

以SVM作为分类器时不同深度CNN模型对应的AUC"

特征提取模型 AUC
CNN(2层) 0.76±0.05
CNN(3层) 0.82±0.03
Alexnet 0.84±0.06

表2

Alexnet模型下不同分类器对应的AU值"

分类器 AUC值
SVM 0.84±0.06
Logistic Regression 0.83±0.03
Softmax 0.85±0.04

表3

Alexnet模型下集成分类器对应的AUC值"

分类器 AUC值
Softmax 0.85±0.04
Random Forest 0.87±0.03
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