Journal of Shandong University(Engineering Science) ›› 2018, Vol. 48 ›› Issue (6): 1-7.doi: 10.6040/j.issn.1672-3961.0.2018.205

• Machine Learning & Data Mining •     Next Articles

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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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

CLC Number: 

  • TP399

Fig.1

Residual learning:a residual block"

Fig.2

Architecture MSResnet"

Fig.3

Multi-scale Residual learning block"

Table 1

The Architectures for MSResnet"

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

Table 2

The demographic feature of dataset"

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

Fig.4

Gray scale transformation diagram"

Fig.5

Before and after image standardization"

Fig.6

Accuracy test and loss test for model"

Fig.7

Comparison of ROC curves for the model"

Table 3

The accuracy performance for each model"

%
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

Table 4

Comparison of accuracy, specificity and sensitive performance of MSResnet with the other technique"

%
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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