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Full Description
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.
Contents
1. Neuroimaging and Deep learning in Stroke Prabha Susy Mathew, Anitha S.Pillai, Ajith Abraham, and Di Biase Lazzaro. 2. Artificial intelligence in Stroke Imaging Lazzaro di Biase, Adriano Bonura, Pasquale Maria Pecoraro, Maria Letizia Caminiti and Vincenzo Di Lazzaro. 3. Applications of Machine Learning and Deep Learning Models in Brain Imaging Analysis Alwin Joseph, Chandra J, Bonny Banerjee, Madhavi Rangaswamy, and Jayasankara Reddy K. 4. A Survey on Deep Learning for Neuroimaging-Based Brain Disorder Analysis Sushil S. Kokare, and Revathy V. R. 5. A Framework for Brain Tumor Image Compression with Principal Component Analysis: Application of Machine Learning in Neuroimaging Subhagata Chattopadhyay. 6. Role of Artificial Intelligence in Neuroimaging for Cognitive Research Meenakshi Malviya, Alwin Joseph, Chandra J, and Pooja V. 7. Machine Learning And Deep Learning In Deep Brain Stimulation Targeting for Parkinson's Disease Dr Vikash Agarwal, Ms Swarna M, and Dr. Dolly Mushahary.
