Machine Learning in Medical Imaging and Computer Vision
Medical images can highlight differences between healthy tissue and unhealthy tissue and these images can then be assessed by a healthcare professional to identify the stage and spread of a disease so a treatment path can be established. With machine learning techniques becoming more prevalent in healthcare, algorithms can be trained to identify healthy or unhealthy tissues and quickly differentiate between the two. Statistical models can be used to process numerous images of the same type in a fraction of the time it would take a human to assess the same quantity, saving time and money in aiding practitioners in their assessment.
This edited book discusses feature extraction processes, reviews deep learning methods for medical segmentation tasks, outlines optimisation algorithms and regularisation techniques, illustrates image classification and retrieval systems, and highlights text recognition tools, game theory, and the detection of misinformation for improving healthcare provision.
Machine Learning in Medical Imaging and Computer Vision provides state of the art research on the integration of new and emerging technologies for the medical imaging processing and analysis fields. This book outlines future directions for increasing the efficiency of conventional imaging models to achieve better performance in diagnoses as well as in the characterization of complex pathological conditions.
The book is aimed at a readership of researchers and scientists in both academia and industry in computer science and engineering, machine learning, image processing, and healthcare technologies and those in related fields.
About the Editors
Amita Nandal is an Associate Professor in the Department of Computer and Communication Engineering, Manipal University Jaipur, India. She has authored or co-authored over 40 scientific articles in the area of image processing, wireless communication, and parallel architectures. Her research interests include image processing, machine learning, deep learning, and digital signal processing.
Liang Zhou is an Associate Professor at the Shanghai University of Medicine & Health Sciences, China. He received his PhD degree from the Donghua University, Shanghai, China, in 2012. His research interests are focused in the areas of big data analysis and machine learning with applications in the field of medicine and healthcare.
Arvind Dhaka is an Associate Professor in the Department of Computer and Communication Engineering, Manipal University Jaipur, India. He has authored or co-authored over 40 scientific articles in the area of image processing, wireless communication, and network security. His research interests include image processing, machine learning, wireless communication, and wireless sensor networks.
Todor Ganchev is a Professor in the Department of Computer Science and Engineering and the Head of the Artificial Intelligence Laboratory at the Technical University of Varna, Bulgaria. He has authored/co-authored over 180 publications in topics, including biometrics, physiological signal processing, machine learning and its applications. He is a senior member of the Institute of Electrical and Electronics Engineers (IEEE).
Farid Nait-Abdesselam is a Professor in the School of Science and Engineering at the University of Missouri Kansas City, USA. His research interests include security and privacy, networking, internet of things, and healthcare systems. He has authored/co-authored over 150 research papers in these areas.
Amita Nandal, Liang Zhou, Arvind Dhaka, Todor Ganchev, Farid Nait-Abdesselam