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山东大学学报 (工学版) ›› 2018, Vol. 48 ›› Issue (6): 1-7.doi: 10.6040/j.issn.1672-3961.0.2018.205

• 机器学习与数据挖掘 •    下一篇

基于多尺度残差神经网络的阿尔茨海默病诊断分类

刘振丙1(),方旭升1,杨辉华1,2,蓝如师1   

  1. 1. 桂林电子科技大学电子工程与自动化学院, 广西 桂林 541000
    2. 北京邮电大学自动化学院, 北京 100876
  • 收稿日期:2018-05-31 出版日期:2018-12-20 发布日期:2018-12-26
  • 作者简介:刘振丙(1980—)，男，山东济宁人，教授，博士生导师，博士，主要研究方向为机器学习与图像处理.E-mail:zbliu@guet.edu.cn
  • 基金资助:
    国家自然科学基金项目(61562013);国家自然科学基金项目(61866009);广西自然科学基金(2017GXNFDA198025)

The diagnosis of Alzheimer's disease classification based on multi-scale residual neutral network

Zhenbing LIU1(),Xusheng FANG1,Huihua YANG1,2,Rushi LAN1   

  1. 1. School of Electronic Engineering and Automation, Guilin University of Electronic Technology, Guilin 541000, Guangxi, China
    2. School of Automation, Beijing University of Electronic Technology, Beijing 100876, China
  • Received:2018-05-31 Online:2018-12-20 Published:2018-12-26
  • Supported by:
    国家自然科学基金项目(61562013);国家自然科学基金项目(61866009);广西自然科学基金(2017GXNFDA198025)

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摘要：

提出多尺度残差神经网络(multi-scale resnet, MSResnet)。采用不同大小的卷积核对图像进行多尺度信息采集,并对神经网络进行残差学习,避免网络退化。对核磁共振图像(magnetic resonance imaging, MRI)进行标准化处理,利用MSResnet模型在阿尔茨海默症(Alzheimer's disease, AD)和正常受试者(normal control, NC)获得的分类准确率为99.41%,在AD和轻度认知障碍(mild cognitive impairment, MCI)获得分类准确率为97.35%。与已有的算法相比,本研究提出的算法的分类准确率得到了明显的提高。

关键词: 多尺度残差神经网络, 核磁共振图像, 阿尔茨海默病, 灰度标准化

Abstract:

A multi-scale resnet (MSResnet) method was proposed in this paper, which employed multi-scale convolution kernel to extract multi-scale information of structural magnetic resonance imaging MRI, and carried out residual learning for neural network, so as to avoid network degradation. After the gray scale standardization of MRI, the 99.41% classification precision was obtained by using the MSResnet model between Alzheimer's disease (AD) and normal control (NC), and the classification accuracy between AD and mild cognitive impairment (MCI) was 97.35%. Compared with the existing approaches, the algorithm proposed in this paper improved the classification accuracy significantly.

Key words: MSResnet, magnetic resonance imaging, Alzheimer's disease, gray scale standardization

中图分类号: 

  • TP399

图1

残差学习:残差块"

图2

多尺度残差网络"

图3

多尺度残差学习块"

表1

多尺度残差模型的框架"

类型 卷积核 尺寸/步长 输出尺寸
卷积 64 7×7/2 112×112×64
最大池化 64 3×3/2 56×56×64
块1 64 Incep+Incep 56×56×64
块2 64 Incep+Incep 56×56×64
块3 128 3×3/2+Incep 28×28×128
块4 128 Incep+Incep 28×28×128
块5 256 3×3/2+Incep 14×14×256
块6 256 Incep+Incep 14×14×256
块7 512 3×3/2+Incep 7×7×512
块8 512 Incep+Incep 7×7×512
平均池化 512 7×7/1 1×1×512

表2

数据集的统计特性"

Subject AD MCI NC
Female/male 98/89 178/113 166/143
Age 76.90±6.15 75.26±7.74 76.71±6.79
MMSE ≤24 ≥24 ≥26
CDR ≥1.0 0.5~1.0 0

图4

灰度转换示意图"

图5

图像标准化前后图"

图6

模型的测试精度和损失"

图7

各模型的ROC曲线"

表3

每一种模型的准确率"

%
模型 AD vs NC AD vs MCI MCI vs NC AD vs NC vs MCI
BA+Resnet 96.51 93.11 84.93 82.36
ST+Resnet 97.45 94.28 85.55 83.51
BA+MSResnet 98.32 95.15 86.70 83.87
ST+SVM 90.16 88.19 80.26 75.26
ST+ELM 91.35 86.46 81.79 77.07
ST+Alexnet 93.49 91.92 83.04 80.30
ST+VGG16 95.83 93.76 84.16 81.25
ST+GoogLenet 97.86 94.49 86.05 83.57
ST+MSResnet 99.41 97.35 87.75 85.53

表4

与其他模型的准确率、敏感度和特异性的比较"

%
模型 AD vs NC AD vs MCI MCI vs NC
准确率 敏感度 特异度 准确率 敏感度 特异度 准确率 敏感度 特异度
Alexnet[17] 96.14 93.57 100.00 90.52 85.11 94.20 84.80 88.02 80.62
VGG[16] 98.33 97.78 98.89 93.89 90.00 97.78 91.67 91.11 92.22
GoogLenet[15] 98.84
CNN[14] 93.08 94.92 92.67 86.30 88.46 84.55 83.30 80.99 85.55
DBN[20] 90.09 94.10 86.12 84.00 89.12 79.12 83.14 95.09 67.26
MSResnet 99.41 97.89 99.86 97.35 93.73 98.51 87.75 84.08 88.49
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