About | Contact Us | Register | Login
ProceedingsSeriesJournalsSearchEAI
phat 23(1):

Research Article

Comparative Analysis of Deep Learning Models for Multiclass Alzheimer’s Disease Classification

Download87 downloads
Cite
BibTeX Plain Text
  • @ARTICLE{10.4108/eetpht.9.4334,
        author={Raghav Agarwal and Abbaraju Sai Sathwik and Deepthi Godavarthi and Janjhyman Venkata Naga Ramesh},
        title={Comparative Analysis of Deep Learning Models for Multiclass Alzheimer’s Disease Classification},
        journal={EAI Endorsed Transactions on Pervasive Health and Technology},
        volume={9},
        number={1},
        publisher={EAI},
        journal_a={PHAT},
        year={2023},
        month={11},
        keywords={Deep learning models, Multiclass classification, Comparative analysis, Transfer learning},
        doi={10.4108/eetpht.9.4334}
    }
    
  • Raghav Agarwal
    Abbaraju Sai Sathwik
    Deepthi Godavarthi
    Janjhyman Venkata Naga Ramesh
    Year: 2023
    Comparative Analysis of Deep Learning Models for Multiclass Alzheimer’s Disease Classification
    PHAT
    EAI
    DOI: 10.4108/eetpht.9.4334
Raghav Agarwal1, Abbaraju Sai Sathwik1, Deepthi Godavarthi1,*, Janjhyman Venkata Naga Ramesh2
  • 1: Vellore Institute of Technology University
  • 2: Koneru Lakshmaiah Education Foundation
*Contact email: deepthi.g@vitap.ac.in

Abstract

INTRODUCTION: The terrible neurological condition is known Worldwide; millions of individuals are affected with Alzheimer's disease (AD). Effective treatment and management of AD depend on early detection and a precise diagnosis. An effective method for identifying anatomical and functional abnormalities in the brain linked to AD is magnetic resonance imaging (MRI). OBJECTIVES: However, manual MRI scan interpretation requires a lot of time and is inconsistent between observers. The automated analysis of MRI images for AD identification and diagnosis using deep learning techniques has shown promise. METHODS: In this paper, we present a convolutional neural network (CNN)-based deep learning model for automatically classifying MRI images for Alzheimer's (AD) and a healthy control group. A huge dataset of MRI scans was used to train the CNN, which distinguished between AD and healthy control groups with excellent accuracy. RESULTS: Additionally, we looked into how transfer learning may be used to enhance pre-trained models and boost CNN performance. We discovered that transfer learning considerably increased the model's accuracy and decreased overfitting. Our findings show that MRI scans may be used to precisely detect and diagnose AD utilizing approaches to deep learning and machine learning. CONCLUSION: These techniques may improve the efficiency and accuracy of AD diagnosis and enable early disease identification, resulting in better AD management and therapy.

Keywords
Deep learning models, Multiclass classification, Comparative analysis, Transfer learning
Received
2023-08-01
Accepted
2023-11-03
Published
2023-11-08
Publisher
EAI
http://dx.doi.org/10.4108/eetpht.9.4334

Copyright © 2023 R. Agarwal et al., licensed to EAI. This is an open access article distributed under the terms of the CC BY-NC-SA 4.0, which permits copying, redistributing, remixing, transformation, and building upon the material in any medium so long as the original work is properly cited.

EBSCOProQuestDBLPDOAJPortico
EAI Logo

About EAI

  • Who We Are
  • Leadership
  • Research Areas
  • Partners
  • Media Center

Community

  • Membership
  • Conference
  • Recognition
  • Sponsor Us

Publish with EAI

  • Publishing
  • Journals
  • Proceedings
  • Books
  • EUDL