This book offers a multidisciplinary perspective and comprehensive coverage of imaging technologies and intelligent algorithms used in medical applications. It focuses on the theory and practical applications of deep learning and traditional machine learning algorithms, specifically geared towards addressing real medical challenges, including feature extraction, segmentation, and multiclass classification of medical images. The book also provides an overview of the principles behind medical imaging technologies that utilize ionizing and non-ionizing radiation, enabling the visualization of internal regions of the human body to identify conditions such as tumors, diseases, injuries, and other anomalies. Coverage also examines the operating principles of these imaging technologies, presents practical case studies, and discusses recent advancements in the field.

Imaging Technologies and Intelligent Algorithms in Medical Applications will appeal to a broad audience, including medical professionals, data scientists, clinical researchers, and academics working at the intersection of healthcare and artificial intelligence. The book offers readers theoretical knowledge and practical insights, thereby enhancing their ability to develop, evaluate, or adopt AI-based medical solutions in research and clinical settings.

Dr. Gabriel Trujillo-Hernández is a professor and researcher at the Universidad Politécnica de Baja California. He received his bachelor's degree in mechatronics engineering, a master's in science, and a Ph.D. from the Autonomous University of Baja California in 2015, 2020, and 2024, respectively.

Jesús Heriberto Orduño-Osuna is a lecturer and researcher at the Universidad Politécnica de Baja California. He received his B.Sc. in mechatronics engineering from the Universidad Politécnica de Baja California (UPBC) in 2020 and his M.Sc. in computational sciences and applied mathematics from Universidad Internacional de La Rioja (UNIR) in 2024. His professional experience spans the post-harvest automation and manufacturing sectors, where he has specialized in automation processes, vision systems, and industrial robotics. His research interests are machine learning, automation and control of industrial processes, microcontrollers, and digital signal processing, with a focus on advancing the integration of intelligent systems in industrial applications.

Dr. María E. Raygoza-Limon is a professor and researcher at the Universidad Politécnica de Baja California and a subject professor at Universidad Autónoma de Baja California. She holds a Ph.D. in chemistry and energy from the Engineering Institute of Universidad Autónoma de Baja California and a Bachelor's degree in industrial engineering with a specialization in Manufacturing.

Dr. Fabian N. Murrieta-Rico is a professor at the Universidad Politécnica de Baja California. He received his B.Eng. and M.Eng. from Instituto Tecnológico de Mexicali (ITM) in 2004 and 2013, respectively. In 2017, he received his Ph.D. in materials physics at Centro de Investigación Científica y Educación Superior de Ensenada (CICESE). He has worked as an automation engineer, systems designer, and university professor. He was a postdoctoral researcher at Facultad de Ingeniería, Arquitectura y Diseño from Universidad Autónoma de Baja California (UABC) and at the Centro de Nanociencias y Nanotecnología from Universidad Nacional Autónoma de México (CNyN-UNAM). His research has been published in journals and presented at international conferences since 2009. He has been a reviewer for several journals, including IEEE Transactions on Industrial Electronics and IEEE Transactions on Instrumentation, Measurement, and Sensor Review. His research interests focus on time and frequency metrology, the design of wireless sensor networks, automated systems, and highly sensitive chemical detectors.