Advances in Deep Learning for Medical Image Analysis
1st Edition
Published on 26. April 2022
168 pages
978-1-000-57599-6 (ISBN)
Description
This reference text introduces the classical probabilistic model, deep learning, and big data techniques for improving medical imaging and detecting various diseases.
The text addresses a wide variety of application areas in medical imaging where deep learning techniques provide solutions with lesser human intervention and reduced time. It comprehensively covers important machine learning for signal analysis, deep learning techniques for cancer detection, diabetic cases, skin image analysis, Alzheimer's disease detection, coronary disease detection, medical image forensic, fetal anomaly detection, and plant phytology. The text will serve as a useful text for graduate students and academic researchers in the fields of electronics engineering, computer science, biomedical engineering, and electrical engineering.
The text addresses a wide variety of application areas in medical imaging where deep learning techniques provide solutions with lesser human intervention and reduced time. It comprehensively covers important machine learning for signal analysis, deep learning techniques for cancer detection, diabetic cases, skin image analysis, Alzheimer's disease detection, coronary disease detection, medical image forensic, fetal anomaly detection, and plant phytology. The text will serve as a useful text for graduate students and academic researchers in the fields of electronics engineering, computer science, biomedical engineering, and electrical engineering.
More details
Edition
1. Auflage
Language
English
Place of publication
London
United Kingdom
Publishing group
Taylor & Francis Ltd
Target group
College/higher education
Professional and scholarly
Product notice
Reflowable
Illustrations
29 Tables, black and white; 50 Line drawings, black and white; 50 Illustrations, black and white
ISBN-13
978-1-000-57599-6 (9781000575996)
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
Other editions
Additional editions
Archana Mire | Vinayak Elangovan | Shailaja Patil
Advances in Deep Learning for Medical Image Analysis
Book
10/2024
1st Edition
CRC Press
€70.90
Shipment within 10-20 days
Archana Mire | Vinayak Elangovan | Shailaja Patil
Advances in Deep Learning for Medical Image Analysis
Book
05/2022
1st Edition
CRC Press
€159.50
Shipment within 10-20 days
Persons
Archana Mire is presently working as Head, Department of computer engineering, Terna Engineering College, Navi Mumbai, India. She has more than 14 years of research and teaching experience. She has published research papers in various SCI/Scopus indexed national/international conferences and journals. She has worked on various national/International conference technical committees and reviewed papers for various conferences and journals. She has served as a session chair for various international conferences organized within and outside India. Her main research area is machine learning and image processing.
Vinayak Elangovan is currently working as an assistant professor of Computer Science at Penn State University in Abington, USA. He earned his Ph.D. in Computer Information Systems Engineering at Tennessee State University, the USA in 2014, and continued his research and teaching there as a Postdoctoral fellow. He worked at The College of New Jersey (TCNJ) and St. Olaf College teaching various computer science courses for undergraduate students during 2015-2017. His research interest includes computer vision, machine vision, multi-sensor data fusion, and activity sequence analysis with a keen interest in software applications development and database management. He has worked on a number of funded projects related to the Department of Defense and the Department of Homeland Security applications. He also has considerable work experience in the engineering and software industries.
Shailaja Patil is currently working as a professor, department of electronics and telecommunication, and Dean (Research and Development), Rajarshi Shahu College of Engineering, Pune, India. She has 25 years of teaching and 3 years of research experience. She has more than 60 publications in peer-reviewed journals. She has delivered expert lectures on WSN, SDN, and Intellectual property Rights at various workshops. She is a Fellow of the Institution of Engineers and members of various professional bodies- IEEE, ISTE, GISFI, ISA, ACM, etc.
Vinayak Elangovan is currently working as an assistant professor of Computer Science at Penn State University in Abington, USA. He earned his Ph.D. in Computer Information Systems Engineering at Tennessee State University, the USA in 2014, and continued his research and teaching there as a Postdoctoral fellow. He worked at The College of New Jersey (TCNJ) and St. Olaf College teaching various computer science courses for undergraduate students during 2015-2017. His research interest includes computer vision, machine vision, multi-sensor data fusion, and activity sequence analysis with a keen interest in software applications development and database management. He has worked on a number of funded projects related to the Department of Defense and the Department of Homeland Security applications. He also has considerable work experience in the engineering and software industries.
Shailaja Patil is currently working as a professor, department of electronics and telecommunication, and Dean (Research and Development), Rajarshi Shahu College of Engineering, Pune, India. She has 25 years of teaching and 3 years of research experience. She has more than 60 publications in peer-reviewed journals. She has delivered expert lectures on WSN, SDN, and Intellectual property Rights at various workshops. She is a Fellow of the Institution of Engineers and members of various professional bodies- IEEE, ISTE, GISFI, ISA, ACM, etc.
Content
1. Introduction. 2. Machine Learning for Signal Analysis. 3. Deep Learning for Cancer Detection. 4. Deep Learning for Diabetic Cases. 5. Deep Learning for Blood Sample Images. 6. Deep Learning for Skin Image Analysis. 7. Deep Learning for Alzheimer's Diseases Detection. 8. Deep Leaning for Coronary Disease Detection. 9. Deep Learning for Medical Image Forensic. 10. Deep Learning for Fetal Anomaly Detection. 11. Digital Detectors in Medicine. 12. Deep Learning for Plant Phytology.
