Keywords
deep learning
diagnosis
brain imaging
convolutional neural networks
magnetic resonance imaging
positron emission tomography
neurodegenerative disorder
cognitive decline
early detection
How to Cite
Abstract
Alzheimer's disease (AD) is a progressive neurodegenerative disorder that primarily affects older adults and is characterized by memory loss and cognitive decline. Early and accurate diagnosis of AD is crucial for effective treatment and management of the disease. Neuroimaging techniques, such as magnetic resonance imaging (MRI) and positron emission tomography (PET), play a vital role in the diagnosis of AD by providing detailed structural and functional information about the brain. However, the interpretation of neuroimaging data for AD diagnosis is challenging and often requires specialized expertise.
Recent advances in deep learning have shown promising results in various medical imaging tasks, including the diagnosis of AD. Deep learning models, particularly convolutional neural networks (CNNs) and recurrent neural networks (RNNs), have the potential to learn complex patterns and representations from neuroimaging data, enabling more accurate and automated diagnosis of AD.
This research investigates deep learning-assisted methods for diagnosing Alzheimer's disease from brain imaging data. We review the existing literature on deep learning approaches for AD diagnosis and highlight the strengths and limitations of these methods. We then propose a novel deep learning architecture for AD diagnosis and evaluate its performance using a publicly available dataset of brain MRI scans.
Our experimental results demonstrate that the proposed deep learning model achieves state-of-the-art performance in AD diagnosis, outperforming existing methods in terms of accuracy and sensitivity. We also discuss the clinical implications of our findings and suggest future research directions in the field of deep learning-assisted diagnosis of Alzheimer's disease.
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