This book is based on deep learning approaches used for the diagnosis of neurological disorders, including basics of deep learning algorithms using diagrams, data tables, and practical examples, for diagnosis of neurodegenerative and neurodevelopmental disorders. It includes application of feed-forward neural networks, deep generative models, convolutional neural networks, graph convolutional networks, and recurrent neural networks in the field of diagnosis of neurological disorders. Along with this, data preprocessing including scaling, correction, trimming, and normalization is also included.






Offers a detailed description of the deep learning approaches used for the diagnosis of neurological disorders.



Demonstrates concepts of deep learning algorithms using diagrams, data tables, and examples for the diagnosis of neurodegenerative, neurodevelopmental, and psychiatric disorders.



Helps build, train, and deploy different types of deep architectures for diagnosis.



Explores data preprocessing techniques involved in diagnosis.



Includes real-time case studies and examples.

This book is aimed at graduate students and researchers in biomedical imaging and machine learning.