International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

Call for Paper Volume 8, Issue 2 (March-April 2026) Submit your research before last 3 days of April to publish your research paper in the issue of March-April.
Submit Research Paper Join as a Reviewer

Plagiarism is checked by the leading plagiarism checker TurnItIn

Call for Paper

Volume 8 Issue 2
March-April 2026

Submit your research paper

Indexing Partners
Academia Advanced Sciences Index Bielefeld Academic Search Engine CiteSeer DRJI Google Scholar Independent Search Engine & Directory Network ISI (International Scientific Indexing) Issuu Mendeley Research Networks
RefSeek ResearcherId - Thomson Reuters ResearchGate Scirus Scribd Semantic Scholar UTeM - Universiti Teknikal Malaysia Melaka Wiki for Call for Papers WorldCat Zenodo

AI-Assisted Multi-Stage Alzheimer's Disease Detection Using DenseNet121 and MRI Imaging with Cognitive Assessment Integration

Author(s) Mr. Konathala Jayavardhan, Prof. Dr. Tarigoppula V S Sriram, Mr. Konderpu Pavan, Ms. Kadali Anuradha
Country India
Abstract Alzheimer's Disease (AD) is a progressive, irreversible neurodegenerative disorder characterized by continuous deterioration in memory, cognitive capabilities, and behavioral functioning. Early and accurate detection of AD is paramount for administering timely clinical interventions to slow disease progression and improve the quality of life for patients and caregivers. Currently, structural Magnetic Resonance Imaging (MRI) is a primary non-invasive modality for identifying cerebral anatomical abnormalities. However, manual interpretation of early-stage structural anomalies is profoundly challenging, extremely time-consuming, and heavily subject to inter-observer variability. The proposed system uniquely integrates deep learning-based visual feature extraction utilizing a transfer-learning optimized DenseNet121 convolutional neural network with standardized clinical cognitive screening via the Montreal Cognitive Assessment (MOCA). Our dual modality approach classifies MRI brain scans into four progressive clinical stages: Non-Demented, Very Mild Demented, Mild Demented, and Moderate Demented. Through comprehensive experimentation on the globally recognized Kaggle Alzheimer MRI Dataset, the optimized DenseNet121 model demonstrated exceptional feature learning capabilities, achieving a training accuracy of 86.9%, a validation accuracy of roughly 76.6%, and a test accuracy of 76.0%.
Keywords Alzheimer's Disease, Deep Learning, DenseNet121, MRI Imaging, Cognitive Assessment, Medical Image Analysis, Explainable AI, Transfer Learning, Clinical Decision Support.
Field Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In Volume 8, Issue 2, March-April 2026
Published On 2026-04-03
View / Download PDF File

Share this



E-ISSN 2582-2160
CrossRef

CrossRef DOI is assigned to each research paper published in our journal.

IJFMR DOI prefix is
10.36948/ijfmr

Downloads
Research Paper Format Copyright Permission Form and Undertaking Form Cover Page Vol 8 Isu 2Cover Page Vol 8 Isu 1Cover Page Vol 7 Isu 6

All research papers published on this website are licensed under Creative Commons Attribution-ShareAlike 4.0 International License, and all rights belong to their respective authors/researchers.

CC-BY-SA Open Access

About IJFMR
Fees & Payment
Current Issue
Publication Archive 		Submit Research Paper
Track Submission Status
Publication Guidelines
Publication Ethics
Peer Review & Plagiarism 		Join as a Reviewer
Editors & Reviewers
Reviewer Referral Program
Get Reviewer Membership Certi. 		Website/Journal Policies
Usage Policy
Content Policies
Privacy Policy
Contact Us +91-9687-828-838 editor@ijfmr.com
Powered by Sky Research Publication and Journals