ItemConference Paper

Enhancing Early Detection of Alzheimers Disease Through Integrated Deep Learning Models: A Multimodal Diagnostic Approach

Title
Enhancing Early Detection of Alzheimers Disease Through Integrated Deep Learning Models: A Multimodal Diagnostic Approach
Creator
Lamani, Manjunath Ramanna; Mukku, Lalasa; Asha, V.; Padmaja, K.
Description
Alzheimer's disease (AD) is a progressive neurodegenerative disorder characterized by cognitive decline and functional impairment. Early detection is crucial for effective management and intervention. This study explores the effectiveness of an integrated deep learning approach combining convolutional neural networks (CNNs) and recurrent neural networks (RNNs) to enhance the early detection of Alzheimer's disease using multimodal data. A novel deep learning model was developed and validated, integrating neuroimaging data (MRI and PET scans) with clinical data using a decision-level fusion strategy. The study utilized a dataset comprising 1000 anonymized patient records from the Alzheimers Disease Neuroimaging Initiative (ADNI). Models were assessed based on accuracy, precision, recall, F1-score, and area under the receiver operating characteristic (ROC) curve (AUC). The integrated model demonstrated superior performance with an accuracy of 95%, precision of 94%, recall of 93%, and an F1-score of 93.5%. The model's AUC was 0.97, indicating excellent diagnostic capability. The proposed deep learning approach significantly improves the early detection of Alzheimers disease by effectively analyzing complex, multimodal data. This model holds considerable potential for clinical applications, providing a robust tool for healthcare professionals to diagnose AD in its early stages. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.
Source
Lecture Notes in Networks and Systems;Volume;1278;pp.1-11
Date
01-01-2025
Publisher
Springer Science and Business Media Deutschland GmbH
Subject
Alzheimers disease; Clinical data integration; Convolutional neural networks; Deep learning; Early detection; Multimodal data; Neuroimaging; Recurrent neural networks
Coverage
Lamani M.R., Department of CSE, CHRIST (Deemed to be University), Kanmanike, Kumbalgudu, Mysore Road, Karnataka, Bangalore, 560074, India; Mukku L., Department of CSE, CHRIST (Deemed to be University), Kanmanike, Kumbalgudu, Mysore Road, Karnataka, Bangalore, 560074, India; Asha V., Department of CSE, RNS Institute of Technology, Bangalore, India; Padmaja K., Department of ISE, GSSSIETW, Mysore, India
Rights
Restricted Access; Hardcopy may be available in the library
Relation
ISSN: 23673370; ISBN: 978-981962702-8;
Format
online
Language
English
Type
Conference paper

Citation

Lamani, Manjunath Ramanna; Mukku, Lalasa; Asha, V.; Padmaja, K., “Enhancing Early Detection of Alzheimers Disease Through Integrated Deep Learning Models: A Multimodal Diagnostic Approach,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 18, 2026, https://archives.christuniversity.in/items/show/25496.

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