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Related Concept Videos

Magnetic Resonance Imaging01:24

Magnetic Resonance Imaging

Magnetic resonance imaging (MRI) is a noninvasive medical imaging technique based on a phenomenon of nuclear physics discovered in the 1930s, in which matter exposed to magnetic fields and radio waves was found to emit radio signals. In 1970, a physician and researcher named Raymond Damadian noticed that malignant (cancerous) tissue gave off different signals than normal body tissue. He applied for a patent for the first MRI scanning device in clinical use by the early 1980s. The early MRI...

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Hybrid PET/MRI Imaging of Alzheimer's Disease Based on 18F-AV-1451
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Magnetic resonance imaging-based Alzheimer's disease detection using an EfficientNet-CMSACCN framework.

S Sujanthi1, A M Ravishankkar2, S Ponmaniraj3

  • 1Department of Computer Science and Engineering, M.Kumarasamy College of Engineering, Karur, Tamil Nadu 639113 India.

Cytotechnology
|June 30, 2026
PubMed
Summary

This study introduces an advanced MRI-based framework using EfficientNet for early Alzheimer's disease (AD) detection. The model achieves near-perfect accuracy, offering a significant improvement for diagnosing this neurodegenerative condition.

Keywords:
Alzheimer’s diseaseCellular neighbours optimizerDeep learningEfficientNetNeuroimaging-based Alzheimer’s classification

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Application of Granger Causality Analysis of the Directed Functional Connection in Alzheimer's Disease and Mild Cognitive Impairment

Published on: August 7, 2017

Area of Science:

  • Neurology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Alzheimer's Disease (AD) presents diagnostic challenges due to varied progression and limitations of single-modality imaging.
  • Current deep learning methods for MRI-based AD diagnosis face issues like data imbalance, high computational cost, and poor generalization.
  • Early detection is crucial for effective management of Alzheimer's Disease.

Purpose of the Study:

  • To develop an efficient MRI-based feature extraction framework for Alzheimer's Disease stage classification.
  • To enhance the accuracy and computational efficiency of deep learning models for AD diagnosis using Magnetic Resonance Imaging (MRI).

Main Methods:

  • An EfficientNet framework was employed for feature extraction from MRI data, leveraging compound scaling and depthwise-separable layers.
  • A Compression-based Multi-Scale Attention Convolutional Network (C-MSACCN) was utilized for classification, incorporating attention and compression strategies.
  • The Improved Cellular Neighbours Optimiser (ICNO) was used for hyperparameter tuning to ensure model robustness and optimal convergence.

Main Results:

  • The proposed model achieved an accuracy, precision, recall, and F1-score of 99.9% on the tested datasets.
  • The framework demonstrated superior performance compared to existing methods for Alzheimer's Disease classification.
  • Visualisation techniques identified disease-relevant brain regions, offering potential clinical insights.

Conclusions:

  • The developed MRI-based EfficientNet framework offers a highly accurate and computationally efficient solution for Alzheimer's Disease stage classification.
  • This approach shows significant promise for improving early AD detection and diagnosis in clinical settings.
  • The integration of advanced deep learning techniques and optimization strategies represents a substantial advancement in neurodegenerative disease diagnostics.