This book explores the current state of the art in AI driven methods to high-performance robust disease diagnosis and organ segmentation in medical image processing. In view of this, computer-aided diagnosis systems have been presented with the goal of strengthening diagnostic imaging and aiding in treatment decision-making. Advanced AI, DL and ML techniques contribute to the diagnosis and prediction of the diseases. As we are heading towards the fast growth of this technology in the daily lives of clinicians, with the introduction of artificial intelligence, deep learning and machine learning which makes each patient unique and leads radiology towards the paradigm of interdisciplinary approach and precise healthcare.
The book explores the frontiers of health informatics, introducing cutting-edge technology and theoretical frameworks. It highlights artificial intelligence, deep learning and machine learning approaches are used to address challenging theories and modalities in the field of medical imaging. It also covers an in-depth assessment of contemporary research and literature, describing a range of extensive learning techniques for medical imaging. Furthermore, the book discusses several health monitoring and patient care strategies. It covers IoT based COVID 19 detection, automatic liver Segmentation, Prediction of Cardiac Health, Lung and breast cancer, Bone Fracture Detection, Dengue incidence rate prediction, Segmentation of Malaria Parasite.
The book is an excellent resource for teaching a graduate course on artificial intelligence in health care. This book is used by interdisciplinary researchers to learn about current advances. This book could be used by industry practitioners who want to use AI techniques to analyze diseases, in addition to its academic use. It provides enough details about state-of-the-art algorithms addressing various biomedical domains, so it could be used by industry practitioners who want to use AI techniques to analyze diseases. Medical institutions utilize this book as a study guide and provide learnings to health professionals on how advanced and powerful AI approaches may help with disease prediction and diagnosis.
This book provides a comprehensive description of the state-of-the-art research and development in the field of Medical Image Processing and Health Informatics and its application in healthcare sector by focusing on various innovation paradigms in system knowledge, intelligence that may be applied to provide realistic solutions to varied health related problems in society.
This book will explore the many aspects of medical imaging and health informatics, as well as how they may be applied to real-world biomedical and healthcare challenges. It will consist of a collection of cutting-edge artificial intelligence and other associated approaches for healthcare and biomedical applications. The book will be a diverse collection of state-of-the-art as well as advancements in various artificial intelligence approaches, and it will be geared at the challenges that healthcare institutions and hospitals face in terms of early detection of diseases, data processing, healthcare monitoring and prognosis of diseases.
Medical imaging and health informatics, is a subfield of science and engineering which applies informatics to medicine and includes study of design, development and application of computational innovations to improve health care. The health domain has a wide range of challenges that can be addressed using computational approaches. Artificial intelligence and associated technologies are becoming more common in society and are often utilised in healthcare. Deep learning algorithms are now giving a promising option for automated disease detection with high accuracy. Clinical data analysis employing deep learning algorithms allows physicians to detect diseases earlier and treat patients more efficiently. It explore such approaches as deep learning algorithms, convolutional neural networks, image processing techniques, and more. These technologies have the potential to transform many aspects of patient care, disease detection, disease progression and pharmaceutical organisations.
This book carves into a wide range of image segmentation, classification, registration, computer-aided analysis applications, methodologies, algorithms, platforms, and tools. The volume gives a holistic perspective of the AI's applied aspect in healthcare through case study and innovative applications. It also depicts how image processing, machine learning and deep learning techniques can be applied for medical diagnostics of several specific health scenarios, such as Covid-19, lung cancer, cardiovascular diseases, breast cancer, liver tumour, bone fracture etc. It also highlights significant issues and concerns in AI for healthcare together with other allied areas such as IoT and medical informatics, to construct a global multidisciplinary forum.
About the Author: Tushar H Jaware, PhD received his degree in Medical Image Processing and is now an assistant professor in the Department of Electronics and Telecommunication Engineering, R C Patel Institute of Technology, Shirpur, India. He has published more than 50 research articles in refereed journals and IEEE conferences, and has three international patents granted and two Indian patents published.
K. Sarat Kumar, PhD received his degree in Electronics Engineering and is now a professor in the Department of Electronics & Communication Engineering, K L University, Andhra Pradesh, India.
Ravindra D Badgujar, PhD received his degree in Electronics Engineering and is now an assistant professor in the Department of Electronics and Telecommunication Engineering, R C Patel Institute of Technology, Shirpur, India. He has published many research articles in refereed journals and IEEE conferences as well as one international patent granted and two Indian patents published.
Svetlin Antonov, PhD received his degree in Telecommunications and is now a lecturer in the Faculty of Telecommunications, TU-Sofia, Bulgaria. He is the author of several books and more than 60 peer-reviewed articles.
