Diagnosis of Neurological Disorders Based on Deep Learning Techniques

Description:

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.

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

Chapter 3 Classification of the Level of Alzheimer’s Disease Using Anatomical Magnetic Resonance Images Based on a Novel Deep Learning Structure

Chapter 4 Detection of Alzheimer’s Disease Stages Based on Deep Learning Architectures from MRI Images

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

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

Chapter 9 Efficient Classification of Schizophrenia EEG Signals Using Deep Learning Methods

Chapter 10 Implementation of a Deep Neural Network-Based Framework for Actigraphy Analysis and Prediction of Schizophrenia

Chapter 11 Evaluating Psychomotor Skills in Autism Spectrum Disorder Through Deep Learning

Chapter 12 Dementia Detection with Deep Networks Using Multi-Modal Image Data

Chapter 13 The Importance of the Internet of Things in Neurolog