Handbook of Deep Learning in Biomedical Engineering and Health Informatics
- Available for pre-order. Item will ship after August 1, 2021
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 the 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. The 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.
- 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.
Table of Contents
1. Review of Existing Systems in Biomedical Using Deep Learning Algorithms
M. Sowmiya, C. Thilagavathi, M. Rajeswari, R. Divya, K. Anusree, and Shyam Krishna
2. An Overview of Convolutional Neural Network Architecture and Its Variants in Medical Diagnostics of Cancer and COVID-19
M. Pavithra, R. Rajmohan, T. Ananth Kumar, and S. G. Sandhya
3. Technical Assessment of Various Image Stitching Techniques: A Deep Learning Approach
M. Anly Antony, R. Satheesh Kumar, G. R.Gnana King, V. Yuvaraj, and Chidambaranathan
4. CCNN: A Deep Learning Approach for an Acute Neurocutaneous Syndrome via Cloud-Based MRI Images
S. Arunmozhi Selvi, T. Ananth Kumar, and R. S. Rajesh
5. Critical Investigation and Prototype Study on Deep Brain Simulations: An Application of Biomedical Engineering in Healthcare
V. Milner Paul, S. R. Boselin Prabhu, T. Jarin, and T. Ananth Kumar
6. Insight into Various Algorithms for Medical Image Analyses Using Convolutional Neural Networks (Deep Learning)
S. Sundaresan, K. Suresh Kumar, V. Kishore Kumar, and B. B. Jaya Kumar
7. Exploration of Deep RNN Architectures: LSTM and GRU in Medical Diagnostics of Cardiovascular and Neuro Diseases
R. Rajmohan, M. Pavithra, T. Ananth Kumar, and P.Manjubala
8. Medical Image Classification and Manifold Disease Identification through Convolutional Neural Networks: A Research Perspective
K. Suresh Kumar, A. S. Radhamani, S. Sundaresan, and T. Ananth Kumar
9. Melonama Detection on Skin Lesion Images Using K-Means Algorithm and SVM Classifier
M. Julie Therese, A. Devi, and G. Kavya
10. Role of Deep Learning Techniques in Detecting Skin Cancer: A Review
S. M. Jaisakthi and B. Devikirubha
11. Deep Learning and Its Applications in Biomedical Image Processing
V. V. Satyanarayana Tallapragada
E. Golden Julie, PhD, is a Senior Assistant Professor in the Department of Computer Science and Engineering at Anna University, Regional Campus, in Tirunelveli, India. She has more than 12 years of experience in teaching and has published more than 34 papers in various international journals. Dr. Julie has also presented more than 20 papers at national and international conferences. She has written ten book chapters and is acting as an editor for the book Successful Implementation and Deployment of IoT Projects in Smart Cities, to be published by IGI Global in the Advances in Environmental Engineering and Green Technologies book series. She is one of the editors for the book Handbook of Research on Blockchain Technology: Trend and Technologies, published by Elsevier. She also acts as a reviewer for many journals on computers and electrical engineering. Dr. Julie has given many guest lectures in various subjects, such as multicore architecture, operating systems, compiler design, etc. She is a recognized reviewer and translator for the NPTEL Online (MOOC) courses certificate from the National Programme on Technology Enhanced Learning. She has acted as a jury member at the national and international levels at IEEE conferences, project fairs, and symposia.
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 and has published more than 50 papers in various international journals. He has presented more than 45 papers in both national and international conferences. He has written four book chapters published in books by Springer and IGI. Dr. Robinson is acting as an editor for a book Successful Implementation and Deployment of IoT Projects in Smart Cities, published by IGI Global in the Advances in Environmental Engineering and Green Technologies book series. He is one of the editors for the book Handbook of Research on Blockchain Technology: Trend and Technologies, published by Elsevier. He has given many guest lectures in various subjects, such as pointer, operating system, compiler design, etc. and has also given an invited talk at a technical symposium. He has acted as a convenor, coordinator, and jury member for IEEE conferences, project fairs, and symposia. His research areas includes wireless sensor networks, ad-hoc networks, soft computing, blockchain, IoT, and image processing. He is a reviewer of many journals, including Multimedia Tools and Applications, and has also published research papers in various SCIE journals.S. M. Jaisakthi, PhD, is an Associate Professor at the School of Computer Science and Engineering at the Vellore Institute of Technology, India. Dr. Jaisakthi has extensive research experience in machine learning in the area of image processing and medical image analysis. She also has significant experience in building deep learning models, including convolutional (CNN) and recurrent neural networks (RNN). 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).