Volume: 1 Issue: 2
Year: 2024, Page: 60-71, Doi: https://doi.org/10.54839/ijeaca.v1i2.7
Received: Oct. 10, 2024 Accepted: Dec. 16, 2024 Published: Dec. 24, 2024
This research aims to advance the diagnosis and management of Alzheimer's disease (AD) by leveraging deep learning methodologies, providing a comprehensive quantitative evaluation of their efficacy compared to traditional machine learning models. A thorough literature review was conducted, focusing on the application of deep learning techniques in AD diagnosis. The study examined various biomarkers and datasets utilized in the field, evaluating their contributions to the accuracy and reliability of diagnostic models. The analysis encompassed both natural language processing and computer vision approaches, highlighting recent trends and innovations. Deep learning models demonstrated superior accuracy in diagnosing AD compared to conventional machine learning techniques. The quantitative analysis revealed significant improvements in early detection and diagnostic precision, showcasing the potential of these advanced methodologies. Despite the advancements, several challenges, such as data variability and model interpretability, were identified, indicating areas for further research. Comparative analysis with existing diagnostic approaches underscored the advancements in accuracy and reliability achieved through deep learning. The novelty of this research lies in its detailed quantitative assessment of deep learning techniques for AD diagnosis, providing a robust foundation for future advancements. Unlike conventional studies, this work offers a comprehensive numerical justification of the efficacy of deep learning models. The integration of diverse biomarkers and datasets, combined with the superior diagnostic performance, sets this study apart, highlighting the potential for significant improvements in AD diagnosis and management through continued innovation in deep learning methodologies.
Keywords: Advancements in Deep Learning for Alzheimer’s Disease Diagnosis: A Comprehensive Review
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Bhosale SS, Dixit VV. (2024). Advancements in Deep Learning for Alzheimer’s Disease Diagnosis: A Comprehensive Review. International Journal of Electronics and Computer Applications. 1(2): 60-71. https://doi.org/10.54839/ijeaca.v1i2.7