This book provides a comprehensive discussion of deep learning (DL) in medical and healthcare applications, including the fundamentals and current advances in medical image analysis, deep learning methods for medical image analysis, and deep learning-based clinical computer-aided diagnosis systems. It further presents algorithms, models, software, and tools in the field of bioinformatics.
This book:
Presents mathematical principles of deep learning algorithms such as convolutional neural networks, and recurrent neural networks. Discusses applications of deep learning such as hyperparameter optimization and multimodal deep learning for bioinformatics.
Showcases how algorithms are applied to a broad range of application areas, including microscopy and pathology.
Covers deep learning techniques such as deep feedforward networks, sequence modeling, and convolutional networks.
Examines the importance of deep learning in biomedical image processing and enhancing biological diagnosis.
It will serve as an ideal reference text for senior undergraduate, graduate students, and academic researchers in the areas such as electrical engineering, electronics, and communications engineering, computer engineering, and information technology.
Reihe
Sprache
Verlagsort
Verlagsgruppe
Zielgruppe
Für höhere Schule und Studium
Postgraduate and Undergraduate Advanced
Illustrationen
21 s/w Photographien bzw. Rasterbilder, 80 s/w Zeichnungen, 101 s/w Abbildungen, 38 s/w Tabellen
38 Tables, black and white; 80 Line drawings, black and white; 21 Halftones, black and white; 101 Illustrations, black and white
Maße
Höhe: 234 mm
Breite: 156 mm
ISBN-13
978-1-032-39292-9 (9781032392929)
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 Klassifikation
T. Ananth kumar is working as Research Head and Associate Professor in Computer Science and Engineering, IFET college of Engineering (Autonomous), India. He received his Ph.D. degree in VLSI Design from Manonmaniam Sundaranar University, Tirunelveli, India. He received his master's degree in VLSI Design from Anna University, Chennai, India and bachelor's degree in Electronics and communication engineering from Anna University, Chennai, India. He has presented papers in various National and International Conferences and Journals. His fields of interest are Networks on Chips, Computer Architecture and ASIC design. He has received awards such as Young Innovator Award, Young Researcher Award, Class A Award - IIT Bombay and Best Paper Award at INCODS 2017. He is a life member of ISTE, Senior Member IEEE and few membership bodies. He has many patents in various domains. He has edited 6 books and has written many book chapters in Springer, IET Press, and Taylor & Francis press. He is the author of the book Evolutionary Intelligence for Healthcare Applications.
Rajmohan Rajendrane earned his Doctoral Degree in Co-operative Networks under Anna University in the year 2022. Rajmohan has spent a decade in instructing, counseling, and down to earth application improvement. He is currently working as Assistant Professor in SRM Institute of Science and Technology (Deemed to be University), Kattankulathur campus, Tamil Nadu, India. His fields of interest are Artificial Intelligence, Data Science, Medical Imaging, Machine Learning, Wireless Network, Deep learning and IoT. He has published more than 60 papers in various reputed SCI, Scopus indexed and UGC care journals. He has published 3 patents in the domain of image processing and has edited 6 with international publishers of repute. He has authored over 20 book chapters and a book titled Evolutionary Intelligence for Healthcare Applications. He received Best Educator Award from International Institute of Organized Research (I2OR), India in 2017. He is the associate editor of PLOS ONE journal and reviewer for reputed international journals.
Niranjanamurthy M is an Associate Professor at the Department of Artificial Intelligence and Machine Learning, BMS Institute of Technology and Management. He completed his PhD in Computer Science. He has fifteen years of teaching experience and two years of industry experience as a Software Engineer. He has written twenty-five books and around hundred articles have been published in various National / International Conferences / International Journals. He has filled thirty-six patents and six of them have been granted. He is also a reviewer and an editorial board member of various International Journals. Alongside, he has been an examiner for doctoral research projects and has conducted various national level workshops and conferences and delivered lectures. He is associated with various professional bodies such as the IEEE, IAENG. The areas of his interest are Data Science, Software Testing, Software Engineering, Web Services, Web-Technologies, Cloud Computing, Big data analytics, and Networking.
Sambasivam Nanasekaran is an Assistant Professor in the School of Computing and Data Science, Xiamen University Malaysia, Sepang, Malaysia. He received a PhD degree in Computer Science and Engineering from Pondicherry University, Puducherry, India. He has held teaching positions in international universities and has also been a dean. He has published research articles in peer reviewed international journals and has presented papers in international conferences. He is also a member of IEEE. He has also been a reviewer in various peer-reviewed Journals and Conferences.
Herausgeber*in
IFET College of Engineering
Department of AI and ML, BMS Institute of Technology and Management, India
1. Healthcare Informatics for Analyzing Patient Health Records. 2. Advancements in Deep Learning for Medical Image Representation Techniques. 3. Secure Steganographic Medical Image Compression by Using Winged Herbi-Hopper Optimization Algorithm. 4. Shedding Light into the Dark: Early Oral Cancer Detection using Hyperspectral Imaging. 5. Seeing the Unseen: An Automated Early Breast Cancer Detection Using Hyperspectral Imaging. 6. Deep Generative Models and Challenges in Synthesizing Histopathological Images for Breast Cancer Diagnosis. 7. Deep Learning-Based Approach for Automated Cataract Detection. 8. Analysis of Vision Health Assessment and Diagnosis Using Advanced Deep Learning Techniques. 9. Diabetic Retinopathy Detection Using Fine-Tuned ResNet-50, ResNet-152, and a Hybrid Classical-Quantum Model: A Comprehensive Deep Learning Approach. 10. Deep Learning for Automated Tumor Segmentation in MRI Images. 11. Neural Models for Embodied AI Agents in Healthcare: Enhancing Patient Interaction, Diagnosis, and Treatment through Autonomous Learning Systems. 12. Embodied AI in Healthcare and Assistive Robotics. 13. Smart Human Intrusion Prevention: YOLO and CNN-Based Detection and Alerting System. 14. Deep Learning for Clinical Decision Support Systems.
