Adversarial Deep Generative Techniques for

Adversarial Deep Generative Techniques for Early Diagnosis of Neurological Conditions and Mental Health Practises: Theoretical Insights with Practical Applications by Abhishek Kumar, Fernando Ortiz-Rodriguez, Jose Braga De Vasconcelos, Pushan Kumar Dutta, Hemant Kumar Saini

• Adversarial Deep Generative Techniques for Early Diagnosis of Neurological Conditions and Mental Health Practises: Theoretical Insights with Practical Applications
• Abhishek Kumar, Fernando Ortiz-Rodriguez, Jose Braga De Vasconcelos, Pushan Kumar Dutta, Hemant Kumar Saini
• Page: 0
• Format: pdf, ePub, mobi, fb2
• ISBN: 9783031911477
• Publisher: Springer-Verlag New York, LLC

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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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Adversarial Deep Generative Techniques for Early Diagnosis of .
This book explores a pioneering exploration of how deep generative models, including generative adversarial networks (GANs) and variational .
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Adversarial Deep Generative Techniques for Early Diagnosis of .
This book explores a pioneering exploration of how deep generative models, including generative adversarial networks (GANs) and variational .

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