Machine Learning and Deep Learning in Neuroimaging Data Analysis

✍ Scribed by Anitha S. Pillai, Bindu Menon

• Publisher: CRC Press
• Year: 2023
• Tongue: English
• Leaves: 133
• Category: Library

✦ Synopsis

Machine Learning (ML) and Deep Learning (DL) have become essential tools in healthcare. They are capable of processing enormous amounts of data to find patterns and are also adopted into methods that manage and make sense of healthcare data, either electronic healthcare records or medical imagery. This book explores how ML/DL can assist neurologists in identifying, classifying or predicting neurological problems that require neuroimaging. With the ability to model high-dimensional datasets, supervised learning algorithms can help in relating brain images to behavioral or clinical observations and unsupervised learning can uncover hidden structures/patterns in images. Bringing together Artificial Intelligence (AI) experts as well as medical practitioners, these chapters cover the majority of neuro problems that use neuroimaging for diagnosis, along with case studies and directions for future research.

✦ Table of Contents

Cover
Half Title
Title Page
Copyright Page
Table of Contents
Contributors
Chapter 1 Neuroimaging and Deep Learning in Stroke Diagnosis: A Review of a Decade of Research
Chapter 2 Artificial Intelligence in Stroke Imaging
Chapter 3 Applications of Machine Learning and Deep Learning Models in Brain Imaging Analysis
Chapter 4 A Survey on Deep Learning for Neuroimaging-Based Brain Disorder Analysis
Chapter 5 A Framework for Brain Tumor Image Compression with Principal Component Analysis: Application of Machine Learning in Neuroimaging
Chapter 6 Role of Artificial Intelligence in Neuroimaging for Cognitive Research
Chapter 7 Machine Learning and Deep Learning in Deep Brain Stimulation Targeting for Parkinson's Disease
Index