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Related Concept Videos

Magnetic Resonance Imaging01:24

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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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Related Experiment Video

Updated: May 30, 2025

Lesion Explorer: A Video-guided, Standardized Protocol for Accurate and Reliable MRI-derived Volumetrics in Alzheimer's Disease and Normal Elderly
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SBERO: Skill Al-Biruni Earth Radius Optimization for Alzheimer's Disease Classification Using Magnetic Resonance

P Gowsikraja1, K Geetha2, C Rajan3

  • 1Department of Computer Science and Design, Kongu Engineering College, Erode, Tamil Nadu, India.

NMR in Biomedicine
|January 31, 2025
PubMed
Summary

This study introduces a new AI model, SBERO_Deep SNN, for classifying Alzheimer's disease (AD) using MRI scans. The model demonstrates high accuracy in early AD detection, offering a promising tool for clinical diagnosis.

Keywords:
Alzheimer's disease (AD)Al‐Biruni Earth Radius (BER)Deep Spiking Neural Network (Deep SNN)Skill Optimization Algorithm (SOA)deep learning (DL)gradient directional patterns (GDP)magnetic resonance imaging (MRI)thresholding transformations

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Area of Science:

  • Neurology
  • Artificial Intelligence
  • Medical Imaging

Background:

  • Alzheimer's disease (AD) is the leading cause of dementia, presenting diagnostic challenges due to overlapping symptoms with normal aging and other cognitive impairments.
  • Accurate and early classification of AD is crucial for timely intervention and management.
  • Current diagnostic methods require improvement in precision, especially for distinguishing early-stage AD.

Purpose of the Study:

  • To develop and evaluate a novel deep learning model for Alzheimer's disease classification using magnetic resonance imaging (MRI).
  • To enhance the accuracy and reliability of AD diagnosis through an optimized deep spiking neural network.
  • To provide a robust tool for differentiating AD from other cognitive disorders, particularly in early disease stages.

Main Methods:

  • Proposed a Skill Al-Biruni Earth Radius Optimization-enabled Deep Spiking Neural Network (SBERO_Deep SNN) for AD classification.
  • Employed thresholding for MRI image enhancement and UNeXT with SBERO optimization for segmentation.
  • Extracted statistical features, Local Binary Patterns (LBP), and Gradient Directional Patterns (GDP) for classification using the SBERO-trained Deep SNN.

Main Results:

  • The SBERO_Deep SNN model achieved high performance metrics: 90.49% accuracy, 89.98% sensitivity, and 90.16% specificity.
  • The proposed method demonstrated superior performance compared to existing state-of-the-art techniques in AD classification.
  • Qualitative analysis confirmed the model's robustness in distinguishing AD from other cognitive conditions, including early-stage cases.

Conclusions:

  • The SBERO_Deep SNN offers a highly accurate and reliable method for classifying Alzheimer's disease using MRI.
  • This novel approach shows significant potential as a clinical diagnostic tool, aiding in the early detection and differentiation of AD.
  • The optimization strategy and deep spiking neural network architecture contribute to improved diagnostic capabilities for neurodegenerative diseases.