Alzheimer's Disease Prediction Using EffNetViTLoRA and BiLSTM with Multimodal Longitudinal MRI Data

• URL: http://arxiv.org/abs/2511.22774v1
• Date: Thu, 27 Nov 2025 21:53:23 GMT
• Title: Alzheimer's Disease Prediction Using EffNetViTLoRA and BiLSTM with Multimodal Longitudinal MRI Data
• Authors: Mahdieh Behjat Khatooni, Mohsen Soryani,
• Abstract summary: Alzheimer's disease (AD) is a prevalent neurodegenerative disorder that progressively impairs memory, decision-making, and overall cognitive function.<n>In this study, we propose a generalized, end-to-end deep learning model for AD prediction using MCI cases from the Alzheimer's Disease Neuroimaging Initiative (ADNI)<n>Our hybrid architecture integrates Convolutional Neural Networks and Vision Transformers to capture both local spatial features and global contextual dependencies from MRI scans.<n>Our multimodal model achieved an average progression prediction accuracy of 95.05% between sMCI and pMCI, outperforming existing studies in AD prediction
• Score: 2.220152876549942
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Alzheimer's disease (AD) is a prevalent neurodegenerative disorder that progressively impairs memory, decision-making, and overall cognitive function. As AD is irreversible, early prediction is critical for timely intervention and management. Mild Cognitive Impairment (MCI), a transitional stage between cognitively normal (CN) aging and AD, plays a significant role in early AD diagnosis. However, predicting MCI progression remains a significant challenge, as not all individuals with MCI convert to AD. MCI subjects are categorized into stable MCI (sMCI) and progressive MCI (pMCI) based on conversion status. In this study, we propose a generalized, end-to-end deep learning model for AD prediction using MCI cases from the Alzheimer's Disease Neuroimaging Initiative (ADNI). Our hybrid architecture integrates Convolutional Neural Networks and Vision Transformers to capture both local spatial features and global contextual dependencies from Magnetic Resonance Imaging (MRI) scans. To incorporate temporal progression, we further employ Bidirectional Long Short-Term Memory (BiLSTM) networks to process features extracted from four consecutive MRI timepoints along with some other non-image biomarkers, predicting each subject's cognitive status at month 48. Our multimodal model achieved an average progression prediction accuracy of 95.05\% between sMCI and pMCI, outperforming existing studies in AD prediction. This work demonstrates state-of-the-art performance in longitudinal AD prediction and highlights the effectiveness of combining spatial and temporal modeling for the early detection of Alzheimer's disease.

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