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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. DL methods apply levels of learning to transform input data into more abstract and composite information. Handbook for Deep Learning in Biomedical Engineering: Techniques and Applications gives readers a complete overview of the essential concepts of DL and its applications in the field of biomedical engineering. DL has been rapidly developed in recent years, in terms of both methodological constructs and practical applications. DL 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 artificial intelligence techniques such as DL and convolutional neural networks to process and evaluate the data. Some examples of biomedical and clinical sensing devices that use DL include computed tomography (CT), magnetic resonance imaging (MRI), ultrasound, single photon emission computed tomography (SPECT), positron emission tomography (PET), magnetic particle imaging, electroencephalography/magnetoencephalography (EE/MEG), optical microscopy and tomography, photoacoustic tomography, electron tomography, and atomic force microscopy. Handbook for Deep Learning in Biomedical Engineering: Techniques and Applications provides the most complete coverage of DL 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, attention deficit hyperactivity disorder (ADHD), and autism spectrum disorder (ASD), tumor prediction, and translational multimodal imaging analysis.
- Presents a comprehensive handbook of the biomedical engineering applications of DL, including computational neuroscience, neuroimaging, time series data such as MRI, functional MRI, CT, EEG, MEG, and data fusion of biomedical imaging data from disparate sources, such as X-Ray/CT.
- Helps readers understand key concepts in DL applications for biomedical engineering and health care, including manifold learning, classification, clustering, and regression in neuroimaging data analysis.
- Provides readers with key DL development techniques such as creation of algorithms and application of DL through artificial neural networks and convolutional neural networks.
- Includes coverage of key application areas of DL such as early diagnosis of specific diseases such as Alzheimer’s, ADHD, and ASD, and 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
- 17th November 2020
- Academic Press
- Paperback ISBN:
- eBook ISBN:
Valentina E. Balas 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 PhD in Applied Electronics and Telecommunications from Polytechnic University of Timisoara. Dr. Balas is author of more than 270 research papers. She is the Editor-in-Chief of 'International Journal of Advanced Intelligence Paradigms' and 'International Journal of Computational Systems Engineering', an Editorial Board member for several other national and international publications, as well as an expert evaluator for national and international projects.
Full Professor, Department of Automatics and Applied Software, Faculty of Engineering, "Aurel Vlaicu" University of Arad, Arad, Romania
Dr. Brojo Kishore Mishra is currently working as a Professor in the Department of Computer Science and Engineering at the GIET University, Gunupur-765022, India. He received his PhD degree in Computer Science from the Berhampur University in 2012. He has published more than 30 research papers in national and international conference proceedings, 25 research papers in peer-reviewed journals, and 22 book chapters; authored 2 books; and edited 4 books. His research interests include data mining, machine learning, soft computing, and security. He has organized and co organized local and international conferences and also edited several special issues for journals. He is the Senior Member of IEEE and Life Member of CSI, ISTE. He is the Editor of CSI Journal of Computing.
Professor, Department of CSE, School of Engineering and Technology, GIET University, India
Dr. Raghvendra Kumar is working as an Associate Professor at the Computer Science and Engineering Department at GIET University, India. He received his BTech, MTech, and PhD in Computer Science and Engineering, India, and Postdoc Fellow from Institute of Information Technology, Virtual Reality and Multimedia, Vietnam. He serves as a Series Editor of Internet of Everything: Security and Privacy Paradigm, Green Engineering and Technology: Concepts and Applications, published by CRC press and Taylor & Francis Group, USA, and Bio-Medical Engineering: Techniques and Applications, published by Apple Academic Press, CRC Press, and Taylor & Francis Group, USA. He is an acquisition editor for Computer Science by Apple Academic Press, CRC Press, and Taylor & Francis Group, USA. He has published a number of research papers in international journal (SCI/SCIE/ESCI/Scopus) and conferences, including IEEE and Springer. He has also served as the organizing chair (RICE 2019, 2020), volume editor (RICE 2018), keynote speaker, session chair, cochair, publicity chair, publication chair, advisory board, technical program committee member in many international and national conferences, and guest editor in many special issues from reputed journals (indexed by Scopus, ESCI, SCI). He also published 13 chapters in edited book published by IGI Global, Springer, and Elsevier. His researches areas are computer networks, data mining, cloud computing and secure multiparty computations, theory of computer science, and design of algorithms. He has authored and edited 23 computer science books in field of Internet of things, data mining, biomedical engineering, big data, and robotics published by IGI Global Publication, USA, IOS Press, Netherlands, Springer, Elsevier, and CRC Press, USA.
Associate Professor, Department of Computer Science and Engineering, School of Engineering and Technology, GIET University, India
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