NEUROVISION-AI: Alzheimer’s Disease Detection Using MRI and Behavioral Data

International Journal of Emerging Research in Science, Engineering, and Management

Vol. 2, Issue 3, pp. 187–193, March 2026

https://doi.org/10.58482/ijersem.v2i3.24

This work is licensed under a Creative Commons Attribution 4.0 International License .

NEUROVISION-AI: Alzheimer’s Disease Detection Using MRI and Behavioral Data

R Priyadarshini, A Chandhana, Vidyala Karthik, Avula Hemanth Kumar Reddy, Tholeti Keerthana Reddy, Kasanna Gari Guru Venkat Sai

Department of CSE, Siddartha Institute of Science and Technology, Puttur, India.

Abstract

Early and accurate detection of Alzheimer’s Disease (AD) is critical for effective intervention, a task where traditional methods often fall short. This paper presents a multimodal deep learning framework to address this diagnostic challenge. A hybrid model combining a Convolutional Neural Network (CNN) to extract spatial features from Magnetic Resonance Imaging (MRI) scans and a Recurrent Neural Network (RNN) to analyze temporal patterns from cognitive assessment data. By fusing these features, the model achieves a more robust classification. Trained on a public dataset, the system classifies AD into four stages (Normal, Mild, Moderate, and Severe) with a validation accuracy of 0.99, demonstrating high precision and recall. This work highlights the potential of hybrid AI models as a powerful diagnostic support tool for clinicians, significantly improving early AD detection.

Keywords: Alzheimer’s Disease, Deep Learning, Early Detection, Multimodal Learning, Convolutional Neural Network.

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DOI: https://doi.org/10.58482/ijersem.v2i3.24

Open Access • Peer Reviewed Article

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This article is part of Volume 2, Issue 3 (March 2026)

2026-03-31