Machine Learning and Deep Learning in Neuroimaging Data Analysis

Anitha S Pillai

ISBN 10: 1032207000 / ISBN 13: 9781032207001

Published by CRC Press Dez 2025, 2025

About this Item

Neuware - 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.

Seller Inventory # 9781032207001

Bibliographic Details

• Title: Machine Learning and Deep Learning in Neuroimaging Data Analysis
• Publisher: CRC Press Dez 2025
• Publication Date: 2025
• Language: English
• ISBN 10: 1032207000
• ISBN 13: 9781032207001
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 254W x 178H x 7D millimeters 254 millimeters width by 178 millimeters height by 7 millimeters depth
• Weight: 245 grams

About this title

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.

About the Author

Anitha S. Pillai is a Professor in the School of Computing Sciences, Hindustan Institute of Technology and Science, India. She obtained a Ph.D. in the field of Natural Language Processing and has three decades of teaching/research experience. She has authored and co-authored several papers in national and international conferences/journals. She is also the Co-founder of AtINeu–Artificial Intelligence in Neurology, focusing on the applications of AI in neurological disorders.

Prof. Bindu Menon is the senior consultant neurologist, Apollo Hospitals, Nellore, Andhra Pradesh, with a teaching experience of 20 years. She has over 90 publications in various international and national journals, and has presented 100 papers and edited 2 books; she has 14 chapters to her credit. She has been conferred with a fellowship from the American Academy of Neurology, World Stroke Organization, Royal College of Physicians (Edinburg), Indian Academy of Neurology, Geriatric Society of India, Indian College of Physicians and Global Association of Physicians of Indian Origin.

She holds various positions and has several international and national awards. She is the founder of Dr. Bindu Menon Foundation and the Co-founder of AtINeu–Artificial Intelligence in Neurology.