Deep Learning in Biomedical Signal and Medical Imaging
1st Edition
Published on 30. September 2024
274 pages
978-1-040-10711-9 (ISBN)
Description
This book offers detailed information on biomedical imaging using Deep Convolutional Neural Networks (Deep CNN). It focuses on different types of biomedical images to enable readers to understand the effectiveness and the potential. It includes topics such as disease diagnosis and image processing perspectives.
Deep Learning in Biomedical Signal and Medical Imaging discusses classification, segmentation, detection, tracking, and retrieval applications of non-invasive methods such as EEG, ECG, EMG, MRI, fMRI, CT, and X-RAY, amongst others. It surveys the most recent techniques and approaches in this field, with both broad coverage and enough depth to be of practical use to working professionals. It includes examples of the application of signal and image processing employing Deep CNN to Alzheimer's, brain tumor, skin cancer, breast cancer, and stroke prediction, as well as ECG and EEG signals. This book offers enough fundamental and technical information on these techniques, approaches, and related problems without overcrowding the reader's head. It presents the results of the latest investigations in the field of Deep CNN for biomedical data analysis. The techniques and approaches presented in this book deal with the most important and/or the newest topics encountered in this field. They combine the fundamental theory of artificial intelligence (AI), machine learning (ML,) and Deep CNN with practical applications in biology and medicine. Certainly, the list of topics covered in this book is not exhaustive, but these topics will shed light on the implications of the presented techniques and approaches on other topics in biomedical data analysis.
The book is written for graduate students, researchers, and professionals in biomedical engineering, electrical engineering, signal process engineering, biomedical imaging, and computer science. The specific and innovative solutions covered in this book for both medical and biomedical applications are critical to scientists, researchers, practitioners, professionals, and educators who are working in the context of the topics.
Deep Learning in Biomedical Signal and Medical Imaging discusses classification, segmentation, detection, tracking, and retrieval applications of non-invasive methods such as EEG, ECG, EMG, MRI, fMRI, CT, and X-RAY, amongst others. It surveys the most recent techniques and approaches in this field, with both broad coverage and enough depth to be of practical use to working professionals. It includes examples of the application of signal and image processing employing Deep CNN to Alzheimer's, brain tumor, skin cancer, breast cancer, and stroke prediction, as well as ECG and EEG signals. This book offers enough fundamental and technical information on these techniques, approaches, and related problems without overcrowding the reader's head. It presents the results of the latest investigations in the field of Deep CNN for biomedical data analysis. The techniques and approaches presented in this book deal with the most important and/or the newest topics encountered in this field. They combine the fundamental theory of artificial intelligence (AI), machine learning (ML,) and Deep CNN with practical applications in biology and medicine. Certainly, the list of topics covered in this book is not exhaustive, but these topics will shed light on the implications of the presented techniques and approaches on other topics in biomedical data analysis.
The book is written for graduate students, researchers, and professionals in biomedical engineering, electrical engineering, signal process engineering, biomedical imaging, and computer science. The specific and innovative solutions covered in this book for both medical and biomedical applications are critical to scientists, researchers, practitioners, professionals, and educators who are working in the context of the topics.
More details
Series
Edition
1. Auflage
Language
English
Place of publication
London
United Kingdom
Publishing group
Taylor & Francis Ltd
Target group
College/higher education
Professional and scholarly
Illustrations
22 Tables, black and white; 28 Line drawings, color; 43 Line drawings, black and white; 14 Halftones, color; 17 Halftones, black and white; 42 Illustrations, color; 60 Illustrations, black and white
ISBN-13
978-1-040-10711-9 (9781040107119)
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
Ngangbam Herojit Singh | Utku Kose | Sarada Prasad Gochhayat
Deep Learning in Biomedical Signal and Medical Imaging
Book
approx. 07/2026
1st Edition
CRC Press
€71.00
Not yet published
Ngangbam Herojit Singh | Utku Kose | Sarada Prasad Gochhayat
Deep Learning in Biomedical Signal and Medical Imaging
Book
09/2024
1st Edition
CRC Press
€252.20
Shipment within 10-20 days
Persons
Ngangbam Herojit Singh is presently working as an Assistant Professor at NIT Agartala in the Department of Computer Science and Engineering. He received his Ph.D. degree from the NIT Manipur, and M.Tech and B.Tech from Anna University, Chennai. His area of interest includes machine learning, biomedical image processing, hybrid intelligent system, and mobile robotics. He has published more than 12 referred journals, including SCI and Scopus-indexed journals. Furthermore, he has published many conference papers and book chapters. Dr. Herojit has participated in many international conferences as an organizer and session chair.
Utku Kose received a B.S. degree in 2008 in computer education from Gazi University, Turkey, as a faculty valedictorian; an M.S. degree in 2010 from Afyon Kocatepe University, Turkey, in the field of computer; and a D.S. / Ph. D. degree in 2017 from Selcuk University, Turkey, in the field of computer engineering. Between 2009 and 2011, he worked as a research assistant at Afyon Kocatepe University. Furthermore, he has also worked as a lecturer and vocational school vice director at Afyon Kocatepe University between 2011 and 2012, as a lecturer and research center director at Usak University between 2012 and 2017, and as an assistant professor at Suleyman Demirel University between 2017 and 2019. Currently, he is an Associate Professor at Suleyman Demirel University, Turkey. He has to his credit more than 200 publications, including articles, authored and edited books, proceedings, and reports. He is also on the editorial boards of many scientific journals and serves as one of the editors of the Biomedical and Robotics Healthcare (CRC Press) book series. His research interest includes artificial intelligence, machine ethics, artificial intelligence safety, biomedical applications, optimization, chaos theory, distance education, e-learning, computer education, and computer science.
Sarada Prasad Gochhayat is currently working as an assistant teaching professor at Villanova University, Philadelphia, USA. He has earned his Ph.D. degree in communication engineering from IISc Bangalore, M. Tech. in Signal Processing from IIT Guwahati, and B. Tech. in ECE from ITER, SOA University, Bhubaneswar. He has past working experience as a faculty member at Manipal University, India, and Old Dominion University, Virginia, USA; post-doctoral research experience at the University of Padua, Italy; and Virginia Modeling, Analysis and Simulation Center, Virginia, USA. His research interests include blockchain, privacy in cloud computing, privacy-preserving data analytics, security and privacy in healthcare systems, and security in IoT and 5G networks.
Utku Kose received a B.S. degree in 2008 in computer education from Gazi University, Turkey, as a faculty valedictorian; an M.S. degree in 2010 from Afyon Kocatepe University, Turkey, in the field of computer; and a D.S. / Ph. D. degree in 2017 from Selcuk University, Turkey, in the field of computer engineering. Between 2009 and 2011, he worked as a research assistant at Afyon Kocatepe University. Furthermore, he has also worked as a lecturer and vocational school vice director at Afyon Kocatepe University between 2011 and 2012, as a lecturer and research center director at Usak University between 2012 and 2017, and as an assistant professor at Suleyman Demirel University between 2017 and 2019. Currently, he is an Associate Professor at Suleyman Demirel University, Turkey. He has to his credit more than 200 publications, including articles, authored and edited books, proceedings, and reports. He is also on the editorial boards of many scientific journals and serves as one of the editors of the Biomedical and Robotics Healthcare (CRC Press) book series. His research interest includes artificial intelligence, machine ethics, artificial intelligence safety, biomedical applications, optimization, chaos theory, distance education, e-learning, computer education, and computer science.
Sarada Prasad Gochhayat is currently working as an assistant teaching professor at Villanova University, Philadelphia, USA. He has earned his Ph.D. degree in communication engineering from IISc Bangalore, M. Tech. in Signal Processing from IIT Guwahati, and B. Tech. in ECE from ITER, SOA University, Bhubaneswar. He has past working experience as a faculty member at Manipal University, India, and Old Dominion University, Virginia, USA; post-doctoral research experience at the University of Padua, Italy; and Virginia Modeling, Analysis and Simulation Center, Virginia, USA. His research interests include blockchain, privacy in cloud computing, privacy-preserving data analytics, security and privacy in healthcare systems, and security in IoT and 5G networks.
Content
1. Detection of Diabetic Retinopathy from retinal fundus images by using CNN Model ResNet-50.
2. DNASNet-RF: Automated Deep NAS-network with Random Forest for Classifying and Detecting Multi-class Brain Tumor. 3. Deep CNNs in image-guided diagnosis of breast and skin cancers. 4. Robust Learning Principle Design to Detect Diabetic Retinopathy Disease in Early Stages with Skilled Feature Extraction Policy. 5. Liver Tumour Detection using Machine Learning Techniques: A Systematic Review. 6. Deep Learning in Photoacoustic Tomographic Image Reconstruction. 7. Design and Development of Computer Aided Diagnosis to Detect Lung Cancer Disease by Using Intelligent Deep Learning Principle. 8. Novel Methodology to Predict and Classify Liver Diseases Based on Hybrid Deep Learning Strategy. 9. Improvements in Analysing Biomedical Signals and Medical Images Using Deep Learning. 10. A Survey on Lung Cancer Diagnosis Using Deep Learning Techniques. 11. Content-Based Medical Image Retrieval using CNN Feature Extraction and Hashing Dimensionality Reduction. 12. Experimental Evaluation of Deep Learning-Assisted Brain Tumor Identification with Advanced Classification Methodology. 13. Study of Biomedical Segmentation Based On Recent Techniques and Deep Learning. 14. Deep CNN in Healthcare. 15. An Improved Multi-Class Breast Cancer Classification and Abnormality Detection Based On Modified Deep Learning Neural Network Principles
2. DNASNet-RF: Automated Deep NAS-network with Random Forest for Classifying and Detecting Multi-class Brain Tumor. 3. Deep CNNs in image-guided diagnosis of breast and skin cancers. 4. Robust Learning Principle Design to Detect Diabetic Retinopathy Disease in Early Stages with Skilled Feature Extraction Policy. 5. Liver Tumour Detection using Machine Learning Techniques: A Systematic Review. 6. Deep Learning in Photoacoustic Tomographic Image Reconstruction. 7. Design and Development of Computer Aided Diagnosis to Detect Lung Cancer Disease by Using Intelligent Deep Learning Principle. 8. Novel Methodology to Predict and Classify Liver Diseases Based on Hybrid Deep Learning Strategy. 9. Improvements in Analysing Biomedical Signals and Medical Images Using Deep Learning. 10. A Survey on Lung Cancer Diagnosis Using Deep Learning Techniques. 11. Content-Based Medical Image Retrieval using CNN Feature Extraction and Hashing Dimensionality Reduction. 12. Experimental Evaluation of Deep Learning-Assisted Brain Tumor Identification with Advanced Classification Methodology. 13. Study of Biomedical Segmentation Based On Recent Techniques and Deep Learning. 14. Deep CNN in Healthcare. 15. An Improved Multi-Class Breast Cancer Classification and Abnormality Detection Based On Modified Deep Learning Neural Network Principles
