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Metaheuristics in Deep Learning in 3D Medical Images for Alzheimer's Disease

Metaheuristics in Deep Learning in 3D Medical Images for Alzheimer's Disease
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Author(s): Dhanush Rachaveti (Vellore Institute of Technology, Chennai, India), M. Subalatha (Heatechs Instruments, India)and R. S. Amutha (Vellore Institute of Technology, Chennai, India)
Copyright: 2026
Pages: 22
Source title: Metaheuristic Algorithms and Optimizing Neural Networks for Biomedical Image Processing
Source Author(s)/Editor(s): Prasanalakshmi Balaji (King Khalid University, Saudi Arabia), K. Martin Sagayam (Karunya Institute of Technology and Sciences, India), Aditi Sharma (Symbiosis International University, India)and Korhen Cengiz (University of Fujairah, UAE)
DOI: 10.4018/979-8-3373-0523-3.ch013

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Abstract

Alzheimer's disease is a progressive disorder that affects primarily memory and thinking abilities and causes the problems of everyday life. Traditional methods of diagnosis are time-consuming and prone to error due to human mistakes. Deep learning systems have proven to be efficient systems for the diagnosis of Alzheimer's disease from brain MRI scans. The systems are based on state-of-the-art machine learning techniques to automatically classify MRI data into various phases of dementia. Deep learning techniques have proven to have high accuracy in classifying these data, particularly using various types of brain imaging information in combination. The diagnosis can be done at a quicker speed using specific algorithms in Deep Learning models that identify nearly optimal solutions. These techniques are also efficient in fine-tuning and feature selection in deep learning models to detect various phases of Alzheimer's disease. Designing lightweight and real-time deep learning models further makes them viable to implement in clinical practice permitting early intervention.

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