Machine Learning and Deep Learning Techniques for Medical Image Recognition
Language: English
Published by Taylor and Francis Ltd, GB, 2025
- Softcover
- New
Seller: Rarewaves.com USA, London, London, United KingdomRarewaves.com USA
AbeBooks seller since June 11, 2025
Condition: New
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Add to basketItem description from seller
Machine Learning and Deep Learning Techniques for Medical Image Recognition 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.Features:Offers important key aspects in the development and implementation of machine learning and deep learning approaches toward developing prediction tools and models and improving medical diagnosis Teaches how machine learning and deep learning algorithms are applied to a broad range of application areas, including chest X-ray, breast computer-aided detection, lung and chest, microscopy, and pathologyCovers common research problems in medical image analysis and their challenges Focuses 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. …
Seller Inventory # LU-9781032432212
- Title
- Machine Learning and Deep Learning Techniques for Medical Image Recognition
- Author
- Ben Othman Soufiene
- Publisher
- Taylor and Francis Ltd, GB
- Publication year
- 2025
- Condition
- New
- Binding
- Paperback
- Language
- English
- ISBN 10
- 1032432217
- ISBN 13
- 9781032432212
- Item weight
- 480 grams
Machine Learning and Deep Learning Techniques for Medical Image Recognition 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.
Features:
- Offers important key aspects in the development and implementation of machine learning and deep learning approaches toward developing prediction tools and models and improving medical diagnosis
- Teaches how machine learning and deep learning algorithms are applied to a broad range of application areas, including chest X-ray, breast computer-aided detection, lung and chest, microscopy, and pathology
- Covers common research problems in medical image analysis and their challenges
- Focuses 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.
"Synopsis" may belong to another edition of this title.
About the Author
Ben Othman Soufiene was Assistant Professor of computer science at the University of Gabes, Tunisia, from 2016 to 2021. He received his PhD in computer science from Manouba University in 2016 for his dissertation on secure data aggregation in wireless sensor networks. He also earned an MS from 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.
Chinmay Chakraborty is Assistant Professor in the Department of Electronics and Communication Engineering, BIT Mesra, India, and a Postdoctoral Fellow of the Federal University of Piauí, Brazil. His primary areas of research include wireless body area networks, Internet of Medical Things (IoMT), point-of-care diagnosis, mHealth/e-health, and medical imaging. Chakraborty is the co-editor of many books on Smart IoMT, healthcare technology, and sensor data analytics.
"About the title" may belong to another edition of this title.
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