Machine Learning in Medical Imaging and Computer Vision
Published on 30. January 2024
Book
Hardback
382 pages
978-1-83953-593-2 (ISBN)
Description
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.
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.
More details
Series
Language
English
Place of publication
Stevenage
United Kingdom
Target group
College/higher education
Professional and scholarly
Product notice
sewn/stitched
Cloth over boards
Dimensions
Height: 234 mm
Width: 156 mm
Thickness: 22 mm
Weight
717 gr
ISBN-13
978-1-83953-593-2 (9781839535932)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Persons
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.
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.
Editor
Associate ProfessorManipal University Jaipur, Department of Computer and Communication Engineering, India
Associate ProfessorShanghai University of Medicine & Health Sciences, Shanghai, China
Associate ProfessorManipal University Jaipur, Department of Computer and Communication Engineering, India
ProfessorTechnical University of Varna, Department of Computer Science and Engineering, Bulgaria
ProfessorUniversity of Missouri Kansas City, School of Science and Engineering, USA
Content
Chapter 1: Machine learning algorithms and applications in medical imaging processing
Chapter 2: Review of deep learning methods for medical segmentation tasks in brain tumors
Chapter 3: Optimization algorithms and regularization techniques using deep learning
Chapter 4: Computer-aided diagnosis in maritime healthcare: review of spinal hernia
Chapter 5: Diabetic retinopathy detection using AI
Chapter 6: A survey image classification using convolutional neural network in deep learning
Chapter 7: Text recognition using CRNN models based on temporal classification and interpolation methods
Chapter 8: Microscopic Plasmodium classification (MPC) using robust deep learning strategies for malaria detection
Chapter 9: Medical image classification and retrieval using deep learning
Chapter 10: Game theory, optimization algorithms and regularization techniques using deep learning in medical imaging
Chapter 11: Data preparation for artificial intelligence in federated learning: the influence of artifacts on the composition of the mammography database
Chapter 12: Spatial cognition by the visually impaired: image processing with SIFT/BRISK-like detector and two-keypoint descriptor on Android CameraX
Chapter 13: Feature extraction process through hypergraph learning with the concept of rough set classification
Chapter 14: Machine learning for neurodegenerative disease diagnosis: a focus on amyotrophic lateral sclerosis (ALS)
Chapter 15: Using deep/machine learning to identify patterns and detecting misinformation for pandemics in the post-COVID-19 era
Chapter 16: Integrating medical imaging using analytic modules and applications
Chapter 2: Review of deep learning methods for medical segmentation tasks in brain tumors
Chapter 3: Optimization algorithms and regularization techniques using deep learning
Chapter 4: Computer-aided diagnosis in maritime healthcare: review of spinal hernia
Chapter 5: Diabetic retinopathy detection using AI
Chapter 6: A survey image classification using convolutional neural network in deep learning
Chapter 7: Text recognition using CRNN models based on temporal classification and interpolation methods
Chapter 8: Microscopic Plasmodium classification (MPC) using robust deep learning strategies for malaria detection
Chapter 9: Medical image classification and retrieval using deep learning
Chapter 10: Game theory, optimization algorithms and regularization techniques using deep learning in medical imaging
Chapter 11: Data preparation for artificial intelligence in federated learning: the influence of artifacts on the composition of the mammography database
Chapter 12: Spatial cognition by the visually impaired: image processing with SIFT/BRISK-like detector and two-keypoint descriptor on Android CameraX
Chapter 13: Feature extraction process through hypergraph learning with the concept of rough set classification
Chapter 14: Machine learning for neurodegenerative disease diagnosis: a focus on amyotrophic lateral sclerosis (ALS)
Chapter 15: Using deep/machine learning to identify patterns and detecting misinformation for pandemics in the post-COVID-19 era
Chapter 16: Integrating medical imaging using analytic modules and applications