Explainable Artificial Intelligence in Medical Imaging: Fundamentals and Applications
Despre această carte electronică
Explainable Artificial Intelligence in Medical Imaging: Fundamentals and Applications focuses on the most recent developments in applying artificial intelligence and data science to health care and medical imaging. Explainable artificial intelligence is a well-structured, adaptable technology that generates impartial, optimistic results. New healthcare applications for explicable artificial intelligence include clinical trial matching, continuous healthcare monitoring, probabilistic evolutions, and evidence-based mechanisms. This book overviews the principles, methods, issues, challenges, opportunities, and the most recent research findings. It makes the emerging topics of digital health and explainable AI in health care and medical imaging accessible to a wide audience by presenting various practical applications.
Presenting a thorough review of state-of-the-art techniques for precise analysis and diagnosis, the book emphasizes explainable artificial intelligence and its applications in healthcare. The book also discusses computational vision processing methods that manage complicated data, including physiological data, electronic medical records, and medical imaging data, enabling early prediction. Researchers, academics, business professionals, health practitioners, and students all can benefit from this book’s insights and coverage.
Despre autor
Amjad Rehman Khan (Senior Member, IEEE) earned a Ph.D. from the Faculty of Computing, Universiti Teknologi Malaysia (UTM), Malaysia, specializing in information security using image processing techniques in 2010. He received a Rector Award for the 2010 Best Student from UTM Malaysia. He is currently associate professor at CCIS Prince Sultan University Riyadh, Saudi Arabia. He is also a principal investigator in several projects and completed projects funded by MoHE Malaysia, Saudi Arabia. His research interests are bioinformatics, IoT, information security, and pattern recognition.
Tanzila Saba (Senior Member, IEEE) received his Ph.D. degree in document information security and management from the Faculty of Computing, Universiti Teknologi Malaysia (UTM), Malaysia, in 2012. She is currently a full professor with the College of Computer and Information Sciences, Prince Sultan University (PSU), Riyadh, Saudi Arabia, and also the leader of the AIDA Laboratory. She has published over 300 publications in high-ranked journals. Her primary research interests include bioinformatics, data mining, and classification using AI models. She received the Best Student Award from the Faculty of Computing, UTM, in 2012 and also received the best researcher award from PSU, from 2013 to 2016. She is the editor of several reputed journals and on a panel of TPC of international conferences.
