(as of Oct 24,2021 15:22:48 UTC – Details)
The rapid proliferation of health-related data has created unparalleled opportunities for patients to improve their health. Medical image segmentation, image annotation, fusion, computer-aided diagnosis, image registration, multimodal image, image-guided therapy, and image database retrieval are few major domains where machine learning is becoming very popular and where failure may be catastrophic. The main purpose of the present edited book entitled “Machine and Deep Learning Approaches for Healthcare Systems” is to advance scientific study in the broad area of machine learning in healthcare, with an emphasis on theory, applications, recent challenges, and cutting-edge techniques. This book also provide a unique compendium of current and emerging machine learning paradigms for healthcare informatics and reflects the diversity, complexity, and the depth and breadth of this multi-disciplinary area. It presents recent research works based on machine learning approaches supported by medical and information communication technologies with the use of data and image analysis. The book “Machine and Deep Learning Approaches for Healthcare Systems” presents insight about techniques that broadly deal in the delivery of quality, accurate and affordable healthcare solutions by predictive, proactive, and preventative methods. Our main goal is to stimulate discussion between ML researchers, technologists, and healthcare domain experts, across academia and industry to meet the common goal of delivering reliable, fair, and high performance ML applications in healthcare.