Short Blurb
This book comprehensively reviews deep learning-based algorithms in medical image analysis problems including medical image processing. It includes a detailed review of deep learning approaches for semantic object detection and segmentation in medical image computing, and large-scale radiology database mining supported by case studies.
Seasonal Blurb
This book comprehensively reviews deep learning-based algorithms in medical image analysis problems including medical image processing. It includes a detailed review of deep learning approaches for semantic object detection and segmentation in medical image computing, and large-scale radiology database mining. A particular focus is placed on the application of convolutional neural networks with the theory, and varied selection of techniques for semantic segmentation using deep learning principles in medical imaging supported by practical examples. This book is aimed at researchers and graduate students in computer engineering, artificial intelligence and machine learning, and biomedical imaging.
Standard Blurb
This book comprehensively reviews deep learning-based algorithms in medical image analysis problems including medical image processing. It includes a detailed review of deep learning approaches for semantic object detection and segmentation in medical image computing, and large-scale radiology database mining. A particular focus is placed on the application of convolutional neural networks with the theory, and varied selection of techniques for semantic segmentation using deep learning principles in medical imaging supported by practical examples.
Offers important key aspects in the development and implementation of ML and DL approaches toward developing prediction tools and models and improving medical diagnosis
Teaches how ML and DL algorithms are applied to a broad range of application areas, including chest x-ray, breast CAD, lung and chest, microscopy, and pathology and so forth
Covers common research problems in medical image analysis and their challenges
Focusses on aspects of deep learning and machine learning for combating COVID-19
Includes pertinent case studies
This book is aimed at researchers and graduate students in computer engineering, artificial intelligence and machine learning, and biomedical imaging.
About the Author: Ben Othman Soufiene is an Assistant Professor of computer science at the University of Gabes, Tunisia from 2016 to 2021. He received his Ph.D. degree in computer science from Manouba University in 2016 for his dissertation on "Secure data aggregation in wireless sensor networks. He also holds M.S. degree from the Monastir University in 2012. His research interests focus on the Internet of Medical Things, Wireless Body Sensor Networks, Wireless Networks, Artificial Intelligence, Machine Learning and Big Data.
Dr. Chinmay Chakraborty is an Assistant Professor in the Department of Electronics and Communication Engineering, BIT Mesra, India, and a Post-doctoral fellow of Federal University of Piauí, Brazil. His primary areas of research include Wireless body area network, Internet of Medical Things, point-of-care diagnosis, mHealth/e-health, and medical imaging. Dr. Chakraborty is co-editing many books on Smart IoMT, Healthcare Technology and Sensor Data Analytics.
