Alzheimer's Disease Early Detection

A Deep Learning Approach with 3D-CNNs and Attention Mechanisms

By: Bolakale Aremu, Charles Johnson Jr.

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Paperback | 28 November 2024

At a Glance

Paperback
164 Pages

Dimensions(cm)
23.5 x 19.1 x 0.89

Paperback

RRP $48.39

$41.90

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"Alzheimer's Disease Early Detection: A Deep Learning Approach with 3D-CNNs and Attention Mechanisms" offers a groundbreaking exploration of how advanced machine learning techniques are revolutionizing the diagnosis of Alzheimer's disease. In this comprehensive guide, you will uncover the powerful role that deep learning, particularly 3D Convolutional Neural Networks (3D-CNNs) and Attention Mechanisms, can play in early detection-paving the way for faster, more accurate diagnoses.

This book is ideal for researchers, students, and professionals in artificial intelligence, neuroscience, and healthcare technology. It provides a detailed, step-by-step breakdown of the algorithms and models used to analyze brain imaging data, specifically focusing on how these techniques outperform traditional diagnostic methods.

You'll dive into key topics such as:

The application of deep learning to brain scans for identifying Alzheimer's at its earliest stages.
The mechanics behind 3D-CNNs and attention mechanisms, which allow for a deeper understanding of brain structures and cognitive decline.
Real-world case studies and cutting-edge research that demonstrate the practical implementation of these techniques.

By the end of this book, you'll gain a comprehensive understanding of how artificial intelligence is reshaping Alzheimer's diagnostics, and how it can be used to improve patient outcomes. Whether you're an AI enthusiast or a healthcare professional, this book is an invaluable resource for those seeking to stay at the forefront of technological advancements in Alzheimer's disease research.

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