Hardcover. This book explores a pioneering exploration of how deep generative models, including generative adversarial networks (GANs) and variational autoencoders (VAEs), renovating early neurological disorder detection. This book is a bridge between computational neuroscience and clinical neurology gaps, providing novel AI-driven methodologies for diagnosing conditions such as Alzheimers, Parkinsons, epilepsy, and neurodevelopmental disorders. With a strong focus on neuroimaging, genomic data analysis, and biomedical informatics, the book equips researchers and practitioners with the tools to improve diagnostic accuracy and decision-making. It includes practical case studies, visual illustrations, and structured methodologies for training and validating deep learning models. Designed for neurologists, radiologists, data scientists, and AI researchers, this book is an essential resource for advancing precision medicine and next-generation healthcare innovation. mso-bidi-language: AR-SA;">This book explores a pioneering exploration of how deep generative models, including generative adversarial networks (GANs) and variational autoencoders (VAEs), renovating early neurological disorder detection. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.

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Detalles bibliogr�ficos

T�tulo: Adversarial Deep Generative Techniques for Early Diagnosis of Neurological Conditions and Mental Health Practises (Hardcover)
Editor: Springer International Publishing AG, Cham
Fecha de publicaci�n: 2025
Idioma: Ingl�s
ISBN 10: 3031911466
ISBN 13: 9783031911460
Encuadernaci�n: Hardcover
Estado: new

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Sinopsis

mso-bidi-language: AR-SA;">This book explores a pioneering exploration of how deep generative models, including generative adversarial networks (GANs) and variational autoencoders (VAEs), renovating early neurological disorder detection.

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This book explores a pioneering exploration of how deep generative models, including generative adversarial networks (GANs) and variational autoencoders (VAEs), renovating early neurological disorder detection. This book is a bridge between computational neuroscience and clinical neurology gaps, providing novel AI-driven methodologies for diagnosing conditions such as Alzheimer’s, Parkinson’s, epilepsy, and neurodevelopmental disorders. With a strong focus on neuroimaging, genomic data analysis, and biomedical informatics, the book equips researchers and practitioners with the tools to improve diagnostic accuracy and decision-making. It includes practical case studies, visual illustrations, and structured methodologies for training and validating deep learning models. Designed for neurologists, radiologists, data scientists, and AI researchers, this book is an essential resource for advancing precision medicine and next-generation healthcare innovation.

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