Diagnosis of Neurological Disorders based on Deep Learning Techniques
- Available for pre-order on April 24, 2023. Item will ship after May 15, 2023
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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 pre-processing including scaling, correction, trimming, 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 disorders; neurodevelopmental, and psychiatric disorders.
Helps build, train, and deploy different types of deep architectures for diagnosis
Explores data pre-processing techniques involved in diagnosis
Include real-time case studies and examples
This book is aimed at graduate students and researchers in biomedical imaging and machine learning.
Table of Contents
Chapter 1. Introduction to deep learning techniques for diagnosis of neurological disorders
Chapter 2. A Comprehensive Study of Data Pre-processing Techniques for Neurological Disease (NLD) Detection
Lakshmi Priya G.G., Sabrina, Sharanya, Laasya, Sunaina, Usha,Chemmalar Selvi G.
Chapter 3. Classification of the level of Alzheimer’s disease using anatomical magnetic resonance images based on a novel deep learning structure
Saif Al-Jumaili, Athar Al-Azzawi, Osman Nuri Uçan, Adil Deniz Duru
Chapter 4. Detection of Alzheimer’s disease stages based on Deep Learning architectures from MRI images
Febin Antony, Dr Anita H B, Jincy A George
Chapter 5. Analysis on Detection of Alzheimer’s using Deep Neural Network
Chapter 6. Detection and Classification of Alzheimer’s disease: A Deep Learning Approach with Predictor variables
Deepthi K. Oommen, J. Arunnehru
Chapter 7. Classification of Brain Tumor using Optimized Deep Neural Network Models
Chapter 8. Fully automated segmentation of brain stroke lesions using mask region-based convolutional neural network
Emre Dandıl, Mehmet Süleyman Yıldırım
Chapter 9. Efficient Classification of Schizophrenia EEG signals using deep learning methods
Subha D. Puthankattil, Marrapu Vynatheya, Ahsan Ali
Chapter 10. Implementation of a Deep Neural Network based framework for Actigraphy analysis and prediction of Schizophrenia
Vijayalakshmi G V Mahesh, Alex Noel Joseph Raj, Chandraprabha R
Chapter 11. Evaluating Psychomotor Skills in Autism Spectrum Disorder through Deep Learning
Ravi Kant Avvari
Chapter 12. Dementia Detection with Deep Networks Using Multi-Modal Image Data
Altuğ Yiğit, Zerrin Işık, Yalın Baştanlar
Chapter 13. The importance of the Internet of Things in Neurological Disorder: A Literature Review
Jyotismita Chaki, Ph.D, is an Assistant Professor in School of Computer Science and Engineering at Vellore Institute of Technology, Vellore, India. She has done 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. She has authored more than forty international conferences and journal papers. She is the author and editor of more than eight books. Currently she is the Academic editor of PLOS ONE journal (SCIE Indexed) and PeerJ Computer Science journal (SCIE Indexed). Associate editor of IET Image Processing Journal (SCIE Indexed), Array journal (Elsevier) and Machine Learning with Applications journal (Elsevier).