Handbook of Deep Learning in Biomedical Engineering and Health Informatics: Biomedical Engineering: Techniques and Applications (Biomecial Engineering: Techniques and Applications)

ISBN 10: 1771889985 / ISBN 13: 9781771889988

Editore: Apple Academic Press, 2021

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Codice articolo 390511040

Dati bibliografici

• Titolo: Handbook of Deep Learning in Biomedical Engineering and Health Informatics: Biomedical Engineering: Techniques and Applications (Biomecial Engineering: Techniques and Applications)
• Editore: Apple Academic Press
• Data di pubblicazione: 2021
• Lingua: Inglese
• ISBN 10: 1771889985
• ISBN 13: 9781771889988
• Rilegatura: Rilegato
• Condizione: New

Descrizione articolo

Riassunto

This new volume discusses state-of-the-art deep learning techniques and approaches that can be applied in biomedical systems and health informatics. Deep learning in the biomedical field is an effective method of collecting and analyzing data that can be used for the accurate diagnosis of disease.

This volume delves into a variety of applications, techniques, algorithms, platforms, and tools used in this area, such as image segmentation, classification, registration, and computer-aided analysis. The editors proceed on the principle that accurate diagnosis of disease depends on image acquisition and interpretation. There are many methods to get high resolution radiological images, but we are still lacking in automated image interpretation. Currently deep learning techniques are providing a feasible solution for automatic diagnosis of disease with good accuracy. Analyzing clinical data using deep learning techniques enables clinicians to diagnose diseases at an early stage and treat patients more effectively.

Chapters explore such approaches as deep learning algorithms, convolutional neural networks and recurrent neural network architecture, image stitching techniques, deep RNN architectures, and more. This volume also depicts how deep learning techniques can be applied for medical diagnostics of several specific health scenarios, such as cancer, COVID-19, acute neurocutaneous syndrome, cardiovascular and neuro diseases, skin lesions and skin cancer, etc.

Key features:

• Introduces important recent technological advancements in the field
• Describes the various techniques, platforms, and tools used in biomedical deep learning systems
• Includes informative case studies that help to explain the new technologies

Handbook of Deep Learning in Biomedical Engineering and Health Informatics provides a thorough exploration of biomedical systems applied with deep learning techniques and will provide valuable information for researchers, medical and industry practitioners, academicians, and students.

Informazioni sull?autore

E. Golden Julie, PhD, is a Senior Assistant Professor in the Department of Computer Science and Engineering at Anna University, Regional Campus, Tirunelveli, India. With more than 12 years of experience in teaching, she has published and presented many papers at national and international conferences. She has written ten book chapters and is co-editor of the books Successful Implementation and Deployment of IoT Projects in Smart Cities and Handbook of Research on Blockchain Technology: Trend and Technologies. She also acts as a reviewer for many journals on computers and electrical engineering.

Y. Harold Robinson, PhD, is currently working at the School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, India. He has more than 15 years of experience in teaching, has published many papers in international journals and presented at both national and international conferences. Along with Dr. Julie, Dr. Robinson is co-editor of the books Successful Implementation and Deployment of IoT Projects in Smart Cities and Handbook of Research on Blockchain Technology: Trend and Technologies. He is a reviewer of many journals.

S. M. Jaisakthi, PhD, is an Associate Professor at the School of Computer Science and Engineering at the Vellore Institute of Technology, Vellore, India. Dr. Jaisakthi has extensive research experience in machine learning in image processing, medical image analysis and in building deep learning models. She has published many research publications in refereed international journals and in proceedings of international conferences. Currently she is investigating a project funded by the Science and Engineering Research Board (SERB).