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Computer-Aided Diagnosis of Alzheimer’s Disease Using Multiple Features with Artificial Neural Network

  • Shih-Ting Yang
  • Jiann-Der Lee
  • Chung-Hsien Huang
  • Jiun-Jie Wang
  • Wen-Chuin Hsu
  • Yau-Yau Wai
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6230)

Abstract

Alzheimer’s disease (AD) is a progressively neuro-degenerative disorder. In the AD-related research, the volumetric analysis of hippocampus is the most extensive study. However, the segmentation and identification of the hippocampus are highly complicated and time-consuming. Therefore, a MRI-based classification framework is proposed to differentiate between AD’s patients and normal individuals. First, volumetric features and shape features were extracted from MRI data. Afterward, Principle component analysis (PCA) was utilized to decrease the dimensions of feature space. Finally, a Back-propagation artificial neural network (ANN) classifier was trained for AD classification. With the proposed framework, the classification accuracy is reached to 88.27% by only using volumetric features and shape features. And, the result achieved up to 92.17% by using volumetric features and shape features with the PCA.

Keywords

Principle Component Analysis Shape Feature Magnetic Resonance Imaging Data Mask Image Volumetric Feature 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Shih-Ting Yang
    • 1
  • Jiann-Der Lee
    • 1
  • Chung-Hsien Huang
    • 1
  • Jiun-Jie Wang
    • 2
  • Wen-Chuin Hsu
    • 3
  • Yau-Yau Wai
    • 3
  1. 1.Department of Electrical EngineeringChang Gung UniversityTaiwan
  2. 2.Department of Medical Imaging and Radiological SciencesChang Gung UniversityTaiwan
  3. 3.Department of NeuroscienceChang Gung Memorial HospitalTaiwan

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