Medical Image Augmentation and Enhancement using Machine Learning and Deep Learning

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Medical Image Augmentation and Enhancement using Machine Learning and Deep Learning is written by Dr. S. Mohankumar and published by Jupiter Publications Consortium. It's available with International Standard Book Number or ISBN identification 9391303404 (ISBN 10) and 9789391303402 (ISBN 13).

About the Book Medical Image Augmentation and Enhancement using Machine Learning and Deep Learning provides a focused, research-oriented overview of how machine learning (ML) and deep learning (DL) methods can be used to increase (augment) and improve (enhance) medical imaging data for downstream tasks such as segmentation and classification. The book’s central purpose is to critically review the medical image analysis literature through the lens of data enrichment—examining how augmentation and enhancement help address common challenges in medical imaging such as limited labelled data, variability across modalities, and the need for high diagnostic precision. It introduces the fundamentals of image analysis and processing, medical imaging concepts and modalities, and the basics of data science, AI, ML, and DL before progressing into deep learning architectures widely applied in medical imaging workflows. A significant portion of the text surveys augmentation techniques—from basic geometric and noise-based transformations to deformable methods and deep learning–driven approaches such as adversarial training, GAN-based synthesis, and neural style transfer—and connects these methods to real use cases in medical image segmentation and classification. The book is positioned as a consolidated reference for researchers and scholars who want a structured starting point and a broad technical map of current approaches in medical image augmentation and enhancement.