Diagnosis of Neurological Disorders Based on Deep Learning Techniques
About this ebook
- 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.
About the author
Jyotismita Chaki, PhD, is an Associate Professor in School of Computer Science and Engineering at Vellore Institute of Technology, Vellore, India. She gained her PhD (Engg.) from Jadavpur University, Kolkata, India. Her research interests include computer vision and image processing, pattern recognition, medical imaging, artificial intelligence, and machine learning. Jyotismita has authored more than 40 international conference and journal papers and is the author and editor of more than eight books. Currently, she is the Academic Editor of PLOS One journal and PeerJ Computer Science journal and Associate Editor of IET Image Processing journal, Array journal, and Machine Learning with Applications journal.
