Machine Learning and Deep Learning in Neuroimaging Data Analysis 1st Edition

Capture the future of brain imaging with Machine Learning and Deep Learning in Neuroimaging Data Analysis, 1st Edition by Anitha S. Pillai and Bindu Menon — a practical, authoritative guide that bridges advanced computational methods and real-world neuroimaging applications.

Step into a clear, structured exploration of how machine learning and deep learning transform MRI, fMRI, EEG and PET data into actionable insights. This book demystifies complex algorithms — from feature extraction and preprocessing pipelines to convolutional neural networks, recurrent models, autoencoders, transfer learning and model interpretability — while grounding each concept in neuroimaging challenges. Rich with case-driven explanations, it helps readers translate theory into reproducible workflows for brain disorder detection, connectivity analysis and predictive modeling.

Designed for neuroscientists, radiologists, clinicians, data scientists and graduate students, this edition emphasizes practical skills: data harmonization, artifact removal, dimensionality reduction, model validation and ethical considerations in medical AI. Whether you are developing diagnostic tools, performing research in cognitive neuroscience, or integrating AI into clinical practice, you’ll find step-by-step guidance and best practices that accelerate learning and reduce trial-and-error.

What sets this work apart is its balanced focus on computational rigor and clinical relevance — making advanced neuroinformatics accessible without sacrificing depth. Readers in India and the global research community will appreciate its relevance to diverse datasets and healthcare settings, with insights that apply to both academic research and translational projects.

Add Machine Learning and Deep Learning in Neuroimaging Data Analysis to your professional library to master the tools shaping modern brain research. A must-read for anyone serious about leveraging AI to understand the brain and improve patient outcomes.

Note: eBooks do not include supplementary materials such as CDs, access codes, etc.