This textbook connects the mathematical foundations of machine learning with the design and evaluation of methods for wound image analysis. It addresses three central tasks: wound classification, wound segmentation, and multimodal analysis combining images with metadata or text.
The chapters progress from wound terminology and evaluation principles to classical classifiers and clustering, convolutional and capsule networks, U-Net and modern segmentation architectures, vision Transformers, and multimodal models. Mathematical derivations, worked numerical examples, explanatory diagrams, practice questions and worked solutions help readers understand how model components operate and how their outputs should be interpreted.
Annotated Python examples and practical research recipes explain data preparation, patient-level data separation, training, model selection, calibration, uncertainty and failure analysis. Particular attention is given to distinguishing improvements in model scores from evidence of clinical usefulness.
Written for advanced students, researchers and engineers in computer vision, biomedical imaging and applied artificial intelligence, the book supports both structured study and selective reference. Familiarity with linear algebra, calculus, probability and basic Python is helpful.
The emphasis is on understanding computations, examining assumptions and developing transparent, reproducible research workflows. The book provides educational and research guidance; its teaching examples and proposed pipelines do not establish clinical effectiveness.
Buy now
- Quote paper
- Massoud Pourmandi (Author), 2026, Machine Learning for Wound Image Analysis, Munich, GRIN Verlag, https://www.grin.com/document/1787813