Towards Accessible Neurodegenerative Disease Diagnostics: Deep Learning Models for Alzheimer’s Detection

Alzheimer’s disease (AD) is a progressive neurodegenerative disorder and the leading cause of dementia globally. Early diagnosis of AD is crucial for initiating timely therapeutic interventions to slow disease progression. However, current diagnostic techniques, such as magnetic resonance imaging (MRI), positron emission tomography (PET), and cerebrospinal fluid (CSF) analysis, face challenges related to cost, accessibility, and reliance on expert interpretation, limiting their utility in resource-constrained regions. Recent advances in deep learning, particularly convolutional neural networks (CNNs), have shown exceptional promise in addressing these challenges by automating the diagnosis process and improving accuracy. Deep learning models excel in identifying subtle, high-dimensional patterns in neuroimaging data that may be overlooked by human experts. This paper explores the application of deep learning approaches for the early and accurate diagnosis of Alzheimer’s disease. By leveraging hierarchical feature representations, these models demonstrate significant potential to revolutionize diagnostic methodologies, making them more accessible and efficient. Our findings highlight the transformative role of artificial intelligence in neurodegenerative disease diagnostics, paving the way for broader adoption in clinical practice.