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 Concept Videos

You might also read

Related Articles

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

Sort by
Same author

Pioneering detonation pressure in energetic materials.

Scientific reports·2026
Same author

Toll-Like Receptor-Mediated Neuroinflammation and Its Role in Neurocognitive Functions.

Current reviews in clinical and experimental pharmacology·2026
Same author

Molecular Docking and Anticancerous Activity of Zinc Oxide Nanoparticles Synthesized From Fruits of Prunus nepalensis Extract.

Biotechnology and applied biochemistry·2026
Same author

Circadian abnormalities, molecular clock gene and chronobiological treatment for psychiatric disorders.

Chronobiology international·2026
Same author

Effect of Light and Stocking Density Regimen on Welfare and Zoo-Technical Performance of Broilers Under Tropical Conditions.

Animal science journal = Nihon chikusan Gakkaiho·2026
Same author

Role of platelet-rich plasma in prevention of port site infection, wound healing and post-operative pain after elective laparoscopic cholecystectomy: A randomised controlled trial.

Journal of minimal access surgery·2026

Related Experiment Video

Updated: Mar 18, 2026

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

2.0K

Claustrum and Hippocampus Segmentation-Based Alzheimer's Disease Identification Model Using RoT-Kmeans and CoLU-CNN.

Praveen Kumar Rai1, Vivek Srivastava2

  • 1Department of Computer Science & Engineering, Dr. APJ Abdul Kalam Technical University, Lucknow, U.P., India. praveen.rai2008@gmail.com.

Molecular Neurobiology
|March 17, 2026
PubMed
Summary

This study introduces an effective Alzheimer's disease (AD) identification model using Collapsing Linear Unit-Convolutional Neural Network (CoLU-CNN) and claustrum segmentation. The novel framework achieved 99% accuracy in classifying AD, improving early diagnosis.

Keywords:
AD classificationAlzheimer’s diseaseClaustrumCoLU-U-NetFourier transform-based Median Filter (FTMF)Hippocampus segmentationRs-fMRITime-series extraction

More Related Videos

A Comprehensive Protocol for Manual Segmentation of the Medial Temporal Lobe Structures
12:30

A Comprehensive Protocol for Manual Segmentation of the Medial Temporal Lobe Structures

Published on: July 2, 2014

21.0K
Three-Dimensional Shape Modeling and Analysis of Brain Structures
05:33

Three-Dimensional Shape Modeling and Analysis of Brain Structures

Published on: November 14, 2019

7.7K

Related Experiment Videos

Last Updated: Mar 18, 2026

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

2.0K
A Comprehensive Protocol for Manual Segmentation of the Medial Temporal Lobe Structures
12:30

A Comprehensive Protocol for Manual Segmentation of the Medial Temporal Lobe Structures

Published on: July 2, 2014

21.0K
Three-Dimensional Shape Modeling and Analysis of Brain Structures
05:33

Three-Dimensional Shape Modeling and Analysis of Brain Structures

Published on: November 14, 2019

7.7K

Area of Science:

  • Neuroscience
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Alzheimer's disease (AD) is a progressive dementia impacting cognitive functions, with early diagnosis crucial for managing its progression.
  • Current AD diagnostic models often overlook the claustrum, a brain region potentially vital for accurate detection.
  • Resting-state functional Magnetic Resonance Imaging (rs-fMRI) offers insights into brain activity relevant for neurological disorder diagnosis.

Purpose of the Study:

  • To propose an effective Alzheimer's disease (AD) identification framework incorporating claustrum segmentation.
  • To enhance AD diagnosis efficiency by focusing on the claustrum, a region previously underexplored in diagnostic models.
  • To develop a novel method for AD classification utilizing advanced machine learning techniques and neuroimaging data.

Main Methods:

  • Preprocessing of resting state-functional Magnetic Resonance Imaging (rs-fMRI) data.
  • Segmentation of brain tissues (Gray Matter, White Matter, Cerebrospinal Fluid) and hippocampus.
  • Claustrum segmentation using Rogers and Tanimoto-based K-means (RoT-Kmeans) on Gray Matter.
  • Feature extraction and selection using Kent Map-based Wild Geese Optimization (KM-WGO).
  • Classification of Alzheimer's disease using a Collapsing Linear Unit-Convolutional Neural Network (CoLU-CNN).

Main Results:

  • The proposed framework successfully segmented the claustrum from rs-fMRI data.
  • Network connectivity was generated and mapped with the segmented claustrum.
  • Optimal features were selected, leading to highly accurate AD classification.
  • The methodology achieved an outstanding 99% accuracy in identifying Alzheimer's disease.

Conclusions:

  • The integration of claustrum segmentation significantly improves AD identification accuracy.
  • The proposed CoLU-CNN based framework demonstrates high efficacy for early and accurate AD diagnosis.
  • This approach offers a promising advancement in neuroimaging-based Alzheimer's disease detection.