Machine Learning and Deep Learning in Neuroimaging Data Analysis

BOOKS - Machine Learning and Deep Learning in Neuroimaging Data Analysis

Machine Learning and Deep Learning in Neuroimaging Data Analysis - Anitha S. Pillai, Bindu Menon 2024 PDF CRC Press BOOKS

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Machine Learning and Deep Learning in Neuroimaging Data Analysis

Author: Anitha S. Pillai, Bindu Menon
Year: 2024
Pages: 133
Format: PDF
File size: 10.1 MB
Language: ENG

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The book "Machine Learning and Deep Learning in Neuroimaging Data Analysis" provides an overview of the current state of machine learning and deep learning techniques in neuroimaging data analysis, highlighting their potential applications and limitations. The book covers topics such as image preprocessing, feature extraction, classification, regression, clustering, dimensionality reduction, and visualization, providing readers with a comprehensive understanding of these techniques and their applications in neuroscience research. The book also discusses the challenges and opportunities of using machine learning and deep learning in neuroimaging data analysis, including the need for large amounts of high-quality data, the importance of careful feature selection, and the potential for overfitting. Additionally, the book explores the future directions of machine learning and deep learning in neuroimaging data analysis, including the development of new algorithms and the integration of multiple modalities. Throughout the book, the authors emphasize the need to study and understand the process of technology evolution, recognizing that the rapid pace of technological advancement can be both empowering and disorienting. They argue that developing a personal paradigm for perceiving the technological process of developing modern knowledge is essential for the survival of humanity and the unification of people in a warring state. The book begins by introducing the basics of machine learning and deep learning, explaining how these techniques can be used to analyze complex datasets and extract meaningful information from large amounts of data. The authors then delve into the specifics of neuroimaging data analysis, highlighting the unique challenges and opportunities presented by this field.

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