Early Detection of Alzheimer’s Disease Using Machine Learning

• Author(s): Om Patkar; Vedant Khorjekar; Rhitikesh Gaikwad; Prof. Salabha Jacob
• Paper ID: 1717501
• Page: 1486-1489
• Published Date: 13-05-2026
• Published In: Iconic Research And Engineering Journals
• Publisher: IRE Journals
• e-ISSN: 2456-8880
• Volume/Issue: Volume 9 Issue 11 May-2026

Abstract

Early detection of Alzheimer’s disease (AD) is critical for timely intervention and slowing disease progression. Manual analysis of MRI scans is labor-intensive and subject to inter-clinician variability. This paper presents an end-to-end deep learning system for automated classification of Alzheimer’s stages using brain MRI images. A Convolutional Neural Network (CNN) is trained to classify images into four categories: Non-Demented, Very Mild Demented, Mild Demented, and Moderate Demented, achieving a test accuracy of 99.84% on 1,276 held-out samples from the Kaggle Alzheimer MRI Dataset (6,376 images total). The system is deployed via a Streamlit web interface for real-time prediction, enhanced with Grad-CAM visualization to highlight discriminative brain regions. Batch processing and timestamped prediction history tracking are also supported. Experimental results demonstrate sub-second inference latency, confirming the system’s potential as a clinical decision-support tool.

Keywords

Alzheimer’s Disease, CNN, Deep Learning, Grad-CAM, MRI, Streamlit

Citations

IRE Journals:
Om Patkar, Vedant Khorjekar, Rhitikesh Gaikwad, Prof. Salabha Jacob "Early Detection of Alzheimer’s Disease Using Machine Learning" Iconic Research And Engineering Journals Volume 9 Issue 11 2026 Page 1486-1489

IEEE:
Om Patkar, Vedant Khorjekar, Rhitikesh Gaikwad, Prof. Salabha Jacob "Early Detection of Alzheimer’s Disease Using Machine Learning" Iconic Research And Engineering Journals, 9(11)