Multimodal Alzheimer’s Disease Detection Using MRI and Cognitive Tests with Deep Learning

Author(s)	Ms. MADUMITHA L, Ms. VIDHIYA S, Mr. NATUDURAI N, Ms. RAMYA J
Country	India
Abstract	Alzheimer’s Disease (AD) is a progressive neurodegenerative disorder that affects memory, cognition, and behavior. Early diagnosis is critical for timely intervention and improving patient outcomes. This paper presents a multi-modal approach for Alzheimer’s detection by integrating MRI brain scan analysis with cognitive and speech-based tests using deep learning models. Convolutional Neural Networks (CNNs) are employed for automated MRI classification, complemented by Grad-CAM visualization for explainability. Cognitive functions are evaluated using reaction time tests, speech memory tests, and speech repetition tests. The proposed system achieves high prediction accuracy and provides an interactive framework for monitoring cognitive decline. Experimental results, including prediction accuracies, confusion matrices, and Grad-CAM heatmaps, demonstrate the effectiveness of the integrated approach.
Field	Medical / Pharmacy
Published In	Volume 8, Issue 1, January-February 2026
Published On	2026-02-10

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International Journal For Multidisciplinary Research