A Novel Multimodal Framework for Early Detection of Alzheimer’s Disease Using Deep Learning
| International Journal of Electronics and Communication Engineering |
| © 2024 by SSRG - IJECE Journal |
| Volume 11 Issue 11 |
| Year of Publication : 2024 |
| Authors : Tatwadarshi P. Nagarhalli, Sanket Patil, Vishal Pande, Uday Aswalekar, Prafulla Patil |
10.14445/23488549/IJECE-V11I11P102
How to Cite?
Tatwadarshi P. Nagarhalli, Sanket Patil, Vishal Pande, Uday Aswalekar, Prafulla Patil, "A Novel Multimodal Framework for Early Detection of Alzheimer’s Disease Using Deep Learning," SSRG International Journal of Electronics and Communication Engineering, vol. 11, no. 11, pp. 12-25, 2024. Crossref, https://doi.org/10.14445/23488549/IJECE-V11I11P102
Abstract:
Alzheimer’s Disease (AD) is a progressive neurodegenerative disorder that poses significant challenges in its early diagnosis, often leading to delayed treatment and poorer outcomes for patients. Traditional diagnostic methods, typically reliant on single data modalities, fall short of capturing the multifaceted nature of the disease. In this paper, we propose a novel multimodal framework for the early detection of AD that integrates data from three primary sources: MRI imaging, cognitive assessments, and biomarkers. This framework employs Convolutional Neural Networks (CNN) for analyzing MRI images and Long Short-Term Memory (LSTM) networks for processing cognitive and biomarker data. The system enhances diagnostic accuracy and reliability by aggregating results from these distinct modalities using advanced techniques like weighted averaging, even in incomplete data. The multimodal approach not only improves the robustness of the detection process but also enables the identification of AD at its earliest stages, offering a significant advantage over conventional methods. The integration of biomarkers and cognitive tests is particularly crucial, as these can detect Alzheimer's long before the onset of clinical symptoms, thereby facilitating earlier intervention and potentially altering the course of the disease. This research demonstrates that the proposed framework has the potential to revolutionize the early detection of AD, paving the way for more timely and effective treatments.
Keywords:
Alzheimer’s Disease (AD) Detection, Early detection of AD, Multimodal framework, MRI imaging, Cognitive assessment, Biomarkers, Machine learning, Deep learning.
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