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Related Experiment Video

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Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
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fMRI-Based Alzheimer's Disease Detection Using the SAS Method with Multi-Layer Perceptron Network.

Aarthi Chelladurai1, Dayanand Lal Narayan2, Parameshachari Bidare Divakarachari3

  • 1Department of Electronics and Communication Engineering, Sengunthar Engineering College, Tiruchengode 637205, Tamil Nadu, India.

Brain Sciences
|June 28, 2023
PubMed
Summary

This study introduces an automated model for early Alzheimer's Disease (AD) diagnosis using functional MRI (fMRI). The novel approach achieves high classification accuracy, improving upon traditional methods for identifying AD and related cognitive impairments.

Keywords:
Alzheimer’s diseaseHoney Badger Optimization AlgorithmMulti-Layer PerceptronSuperpixelsfunctional magnetic resonance imagingnormalization technique

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Area of Science:

  • Neuroimaging and Machine Learning
  • Medical Diagnostics
  • Computational Neuroscience

Background:

  • Alzheimer's Disease (AD) is a leading cause of dementia, necessitating improved diagnostic tools.
  • Current AD diagnosis relies on complex and time-consuming analysis of neuroimaging data, particularly functional Magnetic Resonance Imaging (fMRI).
  • Automated methods are needed to enhance the efficiency and accuracy of early AD detection.

Purpose of the Study:

  • To propose a novel automated model for the early diagnosis of Alzheimer's Disease (AD) using fMRI images.
  • To segment various cognitive states including AD, Normal Controls (NC), Mild Cognitive Impairment (MCI), Early Mild Cognitive Impairment (EMCI), Late Mild Cognitive Impairment (LMCI), and Significant Memory Concern (SMC).
  • To evaluate the performance of the proposed model against conventional methods.

Main Methods:

  • Acquisition of fMRI images from the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset.
  • Application of a normalization technique for image quality enhancement.
  • Segmentation of brain regions using the Segmentation by Aggregating Superpixels (SAS) method.
  • Feature extraction via Gabor and Gray Level Co-Occurrence Matrix (GLCM) techniques.
  • Dimensionality reduction using the Honey Badger Optimization Algorithm (HBOA).
  • Classification using a Multi-Layer Perceptron (MLP) model.

Main Results:

  • The automated model achieved a classification accuracy of 99.44%.
  • Performance metrics included a Dice Similarity Coefficient (DSC) of 88.90%, Jaccard Coefficient (JC) of 90.82%, and Hausdorff Distance (HD) of 88.43%.
  • The proposed model demonstrated superior performance compared to conventional segmentation and classification techniques.

Conclusions:

  • The developed automated model shows high efficacy in the early diagnosis of Alzheimer's Disease and related cognitive impairments using fMRI data.
  • The combination of SAS segmentation, HBOA optimization, and MLP classification offers a robust and accurate approach for neurodegenerative disorder detection.
  • This automated system has the potential to significantly improve the diagnostic process for AD and its spectrum of conditions.