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Deep Learning for Alzheimer's Disease Classification using Texture Features.

Jae-Hong So1, Nuwan Madusanka2, Heung-Kook Choi2

  • 1Department of Digital Anti-Aging Healthcare, u-AHRC, Inje University, Gimhae, Gyeongsangnam, Korea.

Current Medical Imaging Reviews
|February 4, 2020
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Summary

This study introduces a novel Alzheimer's disease (AD) classification method using hippocampus texture analysis and deep learning. The model achieved high accuracy, showing potential as an early diagnostic tool for AD.

Keywords:
Alzheimer's diseaseclassification.deep learninghippocampusimage processingtexture analysis

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Area of Science:

  • Neuroimaging
  • Medical Diagnostics
  • Artificial Intelligence

Background:

  • Alzheimer's disease (AD) significantly impacts the hippocampus.
  • Early and accurate diagnosis of AD is crucial for effective management.

Purpose of the Study:

  • To develop a classification method for Alzheimer's disease (AD) using hippocampal texture analysis.
  • To evaluate the efficacy of a deep learning model for distinguishing between AD, mild cognitive impairment (MCI), and normal controls (NCs).

Main Methods:

  • Utilized magnetic resonance images (MRIs) from the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset.
  • Applied image processing, 3D gray-level co-occurrence matrix (GLCM) texture analysis, and Fisher's coefficient for feature selection.
  • Implemented a deep learning multi-layer perceptron (MLP) model for classification tasks (AD-MCI, AD-NC, MCI-NC).

Main Results:

  • The deep learning model achieved classification accuracies of 72.5% for AD-MCI, 85% for AD-NC, and 75% for MCI-NC.
  • The proposed method demonstrated superior performance, outperforming Support Vector Machine (SVM) and K-nearest neighbor (KNN) classifiers by 6-19%.

Conclusions:

  • The developed texture-based deep learning model is a valid and superior method for Alzheimer's disease classification.
  • This approach shows significant potential as a diagnostic tool for early Alzheimer's detection.