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Deep Learning (DL) is a method of machine learning, running over Artificial Neural Networks, that uses multiple layers to extract high-level features from large amounts of raw data. Deep Learning methods apply levels of learning to transform input data into more abstract and composite information. Handbook of Deep Learning in Biomedical Engineering gives readers a complete overview of the essential concepts of Deep Learning and its applications in the field of Biomedical Engineering. Deep learning has been rapidly developed in recent years, in terms of both methodological constructs and practical applications. Deep Learning provides computational models of multiple processing layers to learn and represent data with higher levels of abstraction. It is able to implicitly capture intricate structures of large-scale data and is ideally suited to many of the hardware architectures that are currently available. The ever-expanding amount of data that can be gathered through biomedical and clinical information sensing devices necessitates the development of machine learning and AI techniques such as Deep Learning and Convolutional Neural Networks to process and evaluate the data. Some examples of biomedical and clinical sensing devices that use Deep Learning include: Computed Tomography (CT), Magnetic Resonance Imaging (MRI), Ultrasound, Single Photon Emission Computed Tomography (SPECT), Positron Emission Tomography (PET), Magnetic Particle Imaging, EE/MEG, Optical Microscopy and Tomography, Photoacoustic Tomography, Electron Tomography, and Atomic Force Microscopy. Handbook of Deep Learning in Biomedical Engineering provides the most complete coverage of Deep Learning applications in biomedical engineering available, including detailed real-world applications in areas such as computational neuroscience, neuroimaging, data fusion, medical image processing, neurological disorder diagnosis for diseases such as Alzheimer’s, ADHD, and ASD, tumor prediction, as well as translational multimodal imaging analysis.
- Presents a comprehensive handbook of the Biomedical Engineering applications of Deep Learning, including computational neuroscience, neuroimaging, Time Series Data such as MRI, fMRI, CT, EEG, MEG, and Data Fusion of biomedical imaging data from disparate sources, such as X-Ray/CT
- Helps readers understand key concepts in Deep Learning applications for Biomedical Engineering and healthcare, including manifold learning, classification, clustering, and regression in Neuroimaging data analysis
- Provides readers with key Deep Learning development techniques such as creation of algorithms and application of Deep Learning through Artificial Neural Networks and Convolutional Neural Networks
- Includes coverage of key application areas of Deep Learning such as early diagnosis of specific diseases such as Alzheimer’s, ADHD, and ASD, as well as tumor prediction through MRI and translational multimodality imaging and biomedical applications such as detection, diagnostic analysis, quantitative measurements, and image guidance of ultrasonography
Graduates, PhD students and lecturers in computer science, biomedical engineering and electrical engineering, as well as scientific researchers in biomedical fields and clinicians
- Application of deep learning in biomedical engineering
2. Applications, algorithms, tools directly related to deep learning
3. Computational Neuroscience; Neuroimaging and Time Series data (including MRI/fMRI/CT, EEG/MEG, etc.) studies;
4. Data Fusion for HealthCare, especially Biomedical images of different nature (X-ray, CT, etc.);
5. Deep neural network in medical image processing (RTG, USG, CT, PET, OCT and others)
6. Early diagnosis of specific diseases like Alzheimer, ADHD, ASD etc
7. Manifold learning, classification, clustering and regression in Neuroimaging data analysis;
8. Multimodal imaging techniques: data acquisition, reconstruction; 2D, 3D, 4D imaging, etc.)
9. Optimization by deep neural networks, Multi-dimensional deep learning
10. Prediction of tumor from MRI using deep learning
11. Theoretical understanding of deep learning in biomedical engineering
12. Translational multimodality imaging and biomedical applications (e.g., detection, diagnostic analysis, quantitative measurements, image guidance of ultrasonography)
- No. of pages:
- © Academic Press 2021
- 1st November 2020
- Academic Press
- Paperback ISBN:
Dr. Valentina E. Balas, Ph. D, is currently Full Professor in the Department of Automatics and Applied Software at the Faculty of Engineering, “Aurel Vlaicu” University of Arad, Romania. She holds a Ph.D. in Applied Electronics and Telecommunications from Polytechnic University of Timisoara. Dr. Balas is author of more than 270 research papers in refereed journals and International Conferences. She is the Editor-in Chief of International Journal of Advanced Intelligence Paradigms (IJAIP) and to International Journal of Computational Systems Engineering (IJCSysE), member in Editorial Board member of several national and international journals and is an evaluator expert for national and international projects. of the Governing Council of Forum for Interdisciplinary Mathematics (FIM), - A Multidisciplinary Academic Body, India.
Full Professor in the Department of Automatics and Applied Software at the Faculty of Engineering, “Aurel Vlaicu” University of Arad, Romania
Brojo Kishore Mishra, Ph.D., is an Associate Professor (IT) and the Institutional IQAC Coordinator at the C. V. Raman College of Engineering (Autonomous), Bhubaneswar, India. Received M.Tech and Ph.D. degrees in Computer Science from the Berhampur University in 2008 and 2012 respectively. Currently he is guiding 05nos. of Ph.D research scholar under Biju Pattnaik University of Technology, Odisha. He has been selected as State Student Coordinator (Odisha) and Regional Student Coordinator (CSI Region – IV) of Computer Society of India in 2015-16 and 2016-17 respectively. Now he has been elected as National Nomination Committee Member (2017-18) for Computer Society of India. Similarly, he has been selected as IEEE Day Ambassador for Kolkata Section of IEEE Region – 10 for the year 2015 and 2016. He was the Jury Coordination Committee Member of All IEEE Young Engineers’ Humanitarian Challenge (AIYEHUM 2015) project competition organized by IEEE Region 10 (Asia pacific). His research interests include Data mining and big data analysis, machine learning, Soft computing, Evolutionary computation. He has already published more than 40 research papers in internationally reputed journals and referred conferences, 7 book chapters, has edited 1 book, and is acting as a member of the editorial board/associate editor / Guest editor of various International journals. He served in the capacity of Keynote Speaker, Plenary Speaker, Program Chair, Proceeding chair, Publicity chair, Special session chairperson and as member of programme committees of many international conferences also. He was associated with a CSI funded research project as a Principal Investigator. He is a life member of ISTE, CSI, and member of IEEE, ACM, IAENG, UACEE, and ACCS.
Department of Information Technology, C. V. Raman College of Engineering, Bhubaneswar, India
Raghvendra Kumar, Ph.D, is an Associate Professor in the Computer Science and Engineering Department at LNCT Group of Colleges, Jabalpur, M.P. India. Dr. Kumar serves as Director of the IT and Data Science Department, Vietnam Center of Research in Economics, Management, Environment (VCREME). He received his Ph.D. in Computer Science and Engineering from Jodhpur National University, India. He serves as Series Editor for Internet of Everything (IOE): Security and Privacy Paradigm published by CRC Press, Taylor & Francis Group, and Bio-Medical Engineering: Techniques and Applications, published by CRC Press, Taylor & Francis Group. He has served as organizing chair (RICE-2019), volume Editor (RICE-2018), Keynote Speaker, session chair, co-chair, publicity chair, publication chair (NGCT-2017), advisory board member, and technical program committee members in many international and national conferences. Dr. Kumar has served as a guest editor for many special journal issues and has published 11 chapters in edited books published by IGI Global, Springer and Elsevier. His research interests include computer networks, Data Mining, cloud computing and Secure Multiparty Computations, Theory of Computer Science and design of algorithms. He has authored and edited 17 computer science books on topics including Internet of Things, data mining, Biomedical Engineering, Big Data, robotics, Graph Theory, and Turing Machine by IGI Global Publication, IOS Press Netherland, Springer, Elsevier, CRC Press and others. He is Managing Editor of the International Journal of Machine Learning and Networked Collaborative Engineering.
Associate Professor in the Computer Science and Engineering Department at LNCT Group of Colleges, Jabalpur, M.P. India
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