Alzheimer disease predicting from clinical and MRI data using DeepALZNET dual pathway framework.

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Abstract

Alzheimer's Disease (AD) is a significant neurological condition characterized by progressive cognitive deterioration, with prevalence rising exponentially with age. Currently, there is no effective cure, and the disease progression impacts patients' quality of life, often leading to severe symptoms before death. The gradual development of AD symptoms, often mistaken for typical aging, frequently leads to delayed diagnosis. This underscores the critical need for precise, early diagnostic methodologies, as timely intervention plays a crucial role in managing progression. Accordingly, this paper introduces DeepALZNET, a dual-pathway computational framework designed to enhance AD prediction by offering two independent processing pathways: one for structured clinical data and another for unstructured brain MRI scans. The first pathway combines a 1D Convolutional Neural Network (CNN) with a Random Forest classifier to analyze clinical data, while the second employs a transfer-learning-based VGG19 architecture to detect subtle structural changes in MRI scans. Empirical validation on two publicly available datasets (2k clinical cases and 40k MRI images) demonstrated that both pathways achieved competitive accuracy ([Formula: see text]), with further evaluation on ADNI and OASIS benchmarks confirming robustness. Unlike recent transformer-based or attention-driven methods, which often demand large multimodal datasets and high computational resources, DeepALZNET prioritizes practical applicability, interpretability, and adaptability, operating effectively with either clinical or imaging data alone. This design bridges the gap between benchmark-driven research and deployable real-world solutions, with potential for multimodal fusion or attention integration in future work.

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