Handbook of Deep Learning in Biomedical Engineering
Techniques and Applications
- 1st Edition - November 12, 2020
- Imprint: Academic Press
- Editors: Valentina Emilia Balas, Brojo Kishore Mishra, Raghvendra Kumar
- Language: English
- Paperback ISBN:9 7 8 - 0 - 1 2 - 8 2 3 0 1 4 - 5
- eBook ISBN:9 7 8 - 0 - 1 2 - 8 2 3 0 4 7 - 3
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… Read more
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Request a sales quoteDeep 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 for Deep Learning in Biomedical Engineering: Techniques and Applications 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 for Deep Learning in Biomedical Engineering: Techniques and Applications 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 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
- Cover image
- Title page
- Table of Contents
- Copyright
- Contributors
- About the editors
- Preface
- Key features
- About the book
- 1. Congruence of deep learning in biomedical engineering: future prospects and challenges
- 1. Introduction
- 2. Fire module
- 3. Background study
- 4. Study of various types of model
- 5. Proposed method by the authors
- 6. Conclusion and future work
- 2. Deep convolutional neural network in medical image processing
- 1. Introduction
- 2. Medical image analysis
- 3. Convolutional neural network and its architectures
- 4. Application of deep convolutional neural network in medical image analysis
- 5. Critical discussion: inferences for future work and limitations
- 6. Conclusion
- 3. Application, algorithm, tools directly related to deep learning
- 1. Introduction
- 2. Tools used in deep learning
- 3. Algorithms
- 4. Applications of deep learning
- 5. Conclusion
- 4. A critical review on using blockchain technology in education domain
- 1. Introduction
- 2. Consortium blockchain and its suitability for e-governance
- 3. Consensus
- 4. Attacks on blockchain
- 5. Blockchain in education domain
- 6. Scalability challenges
- 7. Security challenges
- 8. Conclusion
- 5. Depression discovery in cancer communities using deep learning
- 1. Introduction
- 2. Related work
- 3. Proposed system architecture
- 4. Models
- 5. Conclusion
- 6. Plant leaf disease classification based on feature selection and deep neural network
- 1. Introduction
- 2. Literature review
- 3. Our proposed framework
- 4. Results
- 5. Conclusion
- 7. Early detection and diagnosis using deep learning
- 1. Introduction
- 2. Diagnostics using deep learning
- 3. Early detection of diseases using deep learning
- 4. Conclusion and further advancements
- 8. A review on plant diseases recognition through deep learning
- 1. Introduction
- 2. Plant diseases
- 3. Traditional methods to treat plant diseases
- 4. Innovative detection method
- 5. Remote sensing of plant diseases
- 6. Plant disease detection by well-known deep learning architectures
- 7. Conclusions
- 9. Applications of deep learning in biomedical engineering
- 1. Introduction
- 2. Biomedical engineering
- 3. Deep learning
- 4. Most popular deep neural networks architectures used in biomedical applications
- 5. Convolutional neural network
- 6. Convolution layer
- 7. Pooling layer
- 8. Fully convolutional layer
- 9. Applications of convolutional neural network in biomedicine
- 10. Recurrent neural network
- 11. Applications of recurrent neural network in biomedicine
- 12. Generative adversarial networks
- 13. Applications of generative adversarial network in biomedicine
- 14. Deep belief network
- 15. Pretraining stage
- 16. Fine-tuning stage
- 17. Applications of deep learning in biomedicine
- 18. Biomedical image analysis
- 19. Image detection and recognition
- 20. Image acquisition and image interpretation
- 21. Image segmentation
- 22. Cytopathology and histopathology
- 23. Brain, body, and machine interface
- 24. Classification of the brain–machine interfaces
- 25. Invasive techniques
- 26. Noninvasive techniques
- 27. Body–machine interface
- 28. Drug infusion system
- 29. Rehabilitation system
- 30. Diseases diagnosis
- 31. Omics
- 32. Around the genome
- 33. Protein-binding prediction
- 34. DNA–RNA-binding proteins
- 35. Gene expression
- 36. Alternative splicing
- 37. Gene expression prediction
- 38. Genomic sequencing
- 39. Around the protein
- 40. Protein Structure Prediction
- 41. Protein secondary structure prediction
- 42. Protein Interaction Prediction
- 43. Public and medical health management
- 44. Conclusion
- 10. Deep neural network in medical image processing
- 1. Literature review
- 2. Digital image and computer vision
- 3. Deep learning
- 4. Segmentation techniques in image processing
- 5. Conclusion
- Index
- Edition: 1
- Published: November 12, 2020
- No. of pages (Paperback): 320
- No. of pages (eBook): 320
- Imprint: Academic Press
- Language: English
- Paperback ISBN: 9780128230145
- eBook ISBN: 9780128230473
VE
Valentina Emilia Balas
Valentina Emilia Balas is currently a 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 cum Laude in Applied Electronics and Telecommunications from the Polytechnic University of Timisoara. Dr. Balas is the author of more than 350 research papers. She is the Editor-in-Chief of the 'International Journal of Advanced Intelligence Paradigms' and the 'International Journal of Computational Systems Engineering', an editorial board member for several other national and international publications, and an expert evaluator for national and international projects and PhD theses.
Affiliations and expertise
Full Professor, Department of Automatics and Applied Software, Faculty of Engineering, "Aurel Vlaicu" University of Arad, Arad, RomaniaBM
Brojo Kishore Mishra
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.
Affiliations and expertise
Professor, Department of CSE, School of Engineering and Technology, GIET University, IndiaRK
Raghvendra Kumar
Raghvendra Kumar is working as an Associate Professor in Computer Science and Engineering Department at GIET University, India. He received BTech, MTech, and PhD in Computer Science and Engineering, India, and Postdoc Fellow from the Institute of Information Technology, Virtual Reality and Multimedia, Vietnam. He has published a number of research papers in international journals and conferences. His research areas are computer networks, data mining, cloud computing, and secure multiparty computations, theory of computer science, and design of algorithms. He authored and edited 23 computer science books in field of IoT, data mining, and biomedical engineering.
Affiliations and expertise
Associate Professor, Department of Computer Science and Engineering, GIET University, IndiaRead Handbook of Deep Learning in Biomedical Engineering on ScienceDirect