NEUROVISION-AI: Alzheimer Disease Detection Using Multimodal Deep Learning

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

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