Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Videos

Combined transformer encoder-CNN architecture with texture features for MRI-based Alzheimer's disease detection.

G Divya Jyothi1, C Madan Kumar2, Ravi Kumar Kottala3

  • 1Department of Computer Science and Engineering SR University, Ananthasagar, Hasanparthy, Warangal 506371, India.

Psychiatry Research. Neuroimaging
|July 3, 2026
PubMed
Summary

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Sustainable Gossypium arboreum biodiesel production using an industrial waste heterogeneous catalyst for RCCI engine applications.

Scientific reports·2025
See all related articles

This study introduces a novel deep learning model for early Alzheimer's disease (AD) detection using MRI scans. The combined modified Transformer Encoder-Convolutional Neural Network (CNN) achieved 96% accuracy, improving early diagnosis of Alzheimer's disease.

Area of Science:

  • Neuroimaging
  • Artificial Intelligence in Medicine
  • Biomedical Engineering

Background:

  • Alzheimer's disease (AD) onset precedes clinical symptoms, necessitating early diagnostic methods.
  • Current diagnostic tools often fail to detect subtle early-stage structural brain changes in AD.
  • This gap delays critical medical interventions for Alzheimer's disease.

Purpose of the Study:

  • To propose a novel hybrid deep learning architecture for early Alzheimer's disease detection using MRI.
  • To enhance the accuracy and reliability of Alzheimer's disease diagnosis through advanced image processing and feature extraction.
  • To address the limitations of current diagnostic techniques in identifying early-stage AD.

Main Methods:

  • A combined modified Transformer Encoder-Convolutional Neural Network (CNN) architecture was developed for AD detection.
Keywords:
Alzheimer's disease detectionCNNImproved Gaussian filteringImproved median robust extended local binary patternmodTransEncd

Related Experiment Videos

  • Magnetic Resonance Imaging (MRI) data underwent preprocessing with a Modified Gaussian Filtering technique (modGFT) for noise reduction and edge preservation.
  • Feature extraction included shape features, Improved Median Robust Extended Local Binary Pattern (ImpMRELBP), and Pyramid Histogram of Oriented Gradients (PHOG).
  • Data augmentation techniques were employed to increase the training dataset size and diversity.

    • Main Results:

    • The proposed hybrid modTransEncd-CNN model demonstrated superior performance compared to traditional methods.
    • The model achieved a high accuracy of 96% in classifying Alzheimer's disease.
    • Integration of batch normalization and an enhanced attention module improved model training efficiency and generalization.

    Conclusions:

    • The developed modTransEncd-CNN model offers a promising approach for accurate and early detection of Alzheimer's disease.
    • The hybrid architecture effectively leverages deep learning for analyzing MRI data in AD diagnosis.
    • This advancement has the potential to significantly improve timely intervention strategies for Alzheimer's disease patients.