Alzheimer’s Disease (AD) is a progressive neurodegenerative disorder that affects over 55 million people worldwide, with nearly 10 million new cases reported annually according to the World Health Organization. The disease gradually impairs memory and cognitive abilities, making early detection crucial for effective intervention. Traditionally, Alzheimer’s diagnosis relies heavily on manual assessment of neuroimaging data by neurologists and radiologists, often through visual interpretation of MRI or PET scans. These manual systems are time-consuming, prone to human error, and highly dependent on expert availability. Additionally, manual diagnosis often fails to accurately detect early or intermediate stages, limiting opportunities for timely treatment. Motivated by the need for accurate, rapid, and accessible diagnostic methods, this research proposes an automated system for Alzheimer’s stage classification using MRI images and deep learning techniques, specifically MobileNetV2 integrated with a Random Forest Classifier (RFC). MobileNetV2 efficiently extracts deep and lightweight features from MRI scans, while the RFC enhances classification robustness and generalization. The proposed system preprocesses MRI images, performs deep feature extraction using MobileNetV2, and classifies them into normal, mild cognitive impairment (MCI), and Alzheimer’s stages using RFC. Unlike traditional methods, the approach minimizes subjective bias, reduces diagnostic latency, and improves scalability, making it especially beneficial for rural or underserved healthcare settings. This study aims to enhance diagnostic accuracy while contributing to the development of non-invasive, cost-effective, and intelligent clinical decision support systems. The project integrates key modules including data acquisition, preprocessing, feature extraction, classification, and evaluation, and holds strong potential for real-world deployment in hospitals and research centers for early detection and improved management of Alzheimer’s Disease
Keywords : Alzheimer’s Disease, Structural MRI, Deep Learning, Convolutional Neural Network (CNN), MobileNetV2, Random Forest Classifier (RFC), Early Detection, Mild Cognitive Impairment (MCI), Medical Image Classification, Neurodegenerative Disorders, Feature Extraction, ComputerAided Diagnosis.
Author : Dr.A.P.Sawlikar#1 , Bhoyar Himanshi Anil#2 , Bhagat Tejashri Sanjeev#3 , Mohurle Achal Anil#4 , Pimpalkar Namrata#5 , Lambe Shweta Pravin#6
Title : Convolutional Neural Network for Early Detection of Alzheimer’s Disease Using Structural MRI
Volume/Issue : 2026;03(06)
Page No : 307-315