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.
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 Alzheimers Disease Using Anatomical Magnetic Resonance Images Based
on a Novel Deep Learning Structure
4. Detection of Alzheimers Disease Stages
Based on Deep Learning Architectures from MRI Images
5. Analysis on Detection
of Alzheimers using Deep Neural Network
6. Detection and Classification of
Alzheimers 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.
