Diagnosis of Neurological Disorders Based on Deep Learning Techniques

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.

1. Introduction to Deep Learning Techniques for Diagnosis of Neurological Disorders 2. A Comprehensive Study of Data Pre-Processing Techniques for Neurological Disease (NLD) Detection 3. Classification of the Level of Alzheimer’s Disease Using Anatomical Magnetic Resonance Images Based on a Novel Deep Learning Structure 4. Detection of Alzheimer’s Disease Stages Based on Deep Learning Architectures from MRI Images 5. Analysis on Detection of Alzheimer’s using Deep Neural Network 6. Detection and Classification of Alzheimer’s Disease: A Deep Learning Approach with Predictor Variables 7. Classification of Brain Tumor Using Optimized Deep Neural Network Models 8. Fully Automated Segmentation of Brain Stroke Lesions Using Mask Region-Based Convolutional Neural Network 9. Efficient Classification of Schizophrenia EEG Signals Using Deep Learning Methods 10. Implementation of a Deep Neural Network-Based Framework for Actigraphy Analysis and Prediction of Schizophrenia 11. Evaluating Psychomotor Skills in Autism Spectrum Disorder Through Deep Learning 12. Dementia Detection with Deep Networks Using Multi-Modal Image Data 13. The Importance of the Internet of Things in Neurological Disorder: A Literature Review

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.