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Diagnostic Biomedical Signal and Image Processing Applications With Deep Learning Methods
- 1st Edition - April 30, 2023
- Editors: Kemal Polat, Saban Öztürk
- Language: English
- Paperback ISBN:9 7 8 - 0 - 3 2 3 - 9 6 1 2 9 - 5
- eBook ISBN:9 7 8 - 0 - 3 2 3 - 9 9 6 8 1 - 5
Diagnostic Biomedical Signal and Image Processing Applications with Deep Learning Methods presents comprehensive research on both medical imaging and medical signals analysis.… Read more
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Request a sales quoteDiagnostic Biomedical Signal and Image Processing Applications with Deep Learning Methods presents comprehensive research on both medical imaging and medical signals analysis. The book discusses classification, segmentation, detection, tracking and retrieval applications of non-invasive methods such as EEG, ECG, EMG, MRI, fMRI, CT and X-RAY, amongst others. These image and signal modalities include real challenges that are the main themes that medical imaging and medical signal processing researchers focus on today. The book also emphasizes removing noise and specifying dataset key properties, with each chapter containing details of one of the medical imaging or medical signal modalities.
Focusing on solving real medical problems using new deep learning and CNN approaches, this book will appeal to research scholars, graduate students, faculty members, R&D engineers, and biomedical engineers who want to learn how medical signals and images play an important role in the early diagnosis and treatment of diseases.
- Investigates novel concepts of deep learning for acquisition of non-invasive biomedical image and signal modalities for different disorders
- Explores the implementation of novel deep learning and CNN methodologies and their impact studies that have been tested on different medical case studies
- Presents end-to-end CNN architectures for automatic detection of situations where early diagnosis is important
- Includes novel methodologies, datasets, design and simulation examples
Graduate students, researchers, and professionals in biomedical engineering, electrical engineering, signal process engineering, biomedical imaging, and computer science
- Cover image
- Title page
- Table of Contents
- Copyright
- List of contributors
- Chapter 1. Introduction to deep learning and diagnosis in medicine
- Abstract
- Introduction
- Deep learning architectures
- Application fields of deep learning in medicine
- Conclusions
- References
- Chapter 2. One-dimensional convolutional neural network-based identification of sleep disorders using electroencephalogram signals
- Abstract
- Introduction
- Materials and methods
- Results
- Discussions
- Conclusions
- References
- Chapter 3. Classification of histopathological colon cancer images using particle swarm optimization-based feature selection algorithm
- Abstract
- Introduction
- Methodology
- Results
- Discussion
- Conclusion
- References
- Chapter 4. Arrhythmia diagnosis from ECG signal pulses with one-dimensional convolutional neural networks
- Abstract
- Introduction
- Definition of problem
- Materials and methods
- Experimental result
- Discussion
- Conclusion and future direction
- References
- Chapter 5. Patch-based approaches to whole slide histologic grading of breast cancer using convolutional neural networks
- Abstract
- Introduction and motivation
- Challenges in obtaining Nottingham grading score
- Literature review and state of the art
- Proposed methodology
- Results and discussions
- Conclusions
- Future work
- References
- Chapter 6. Deep neural architecture for breast cancer detection from medical CT image modalities
- Abstract
- Introduction
- Related work
- Experimental work
- Experimental results
- Conclusion
- References
- Chapter 7. Automated analysis of phase-contrast optical microscopy time-lapse images: application to wound healing and cell motility assays of breast cancer
- Abstract
- Introduction and motivation
- Literature review and state of the art
- Problem definition, acquisition and annotation of data
- Proposed solution
- Qualitative and quantitative analysis
- Use cases and applications
- Discussion
- Conclusions
- Outlook and future work
- Software availability
- Acknowledgment
- References
- Chapter 8. Automatic detection of pathological changes in chest X-ray screening images using deep learning methods
- Abstract
- Introduction
- Screening for lung abnormalities
- Detecting extrapulmonary pathologies
- Identification of subjects with lung roots abnormalities
- Chest X-ray image analysis web services
- Conclusion
- References
- Chapter 9. Dependence of the results of adversarial attacks on medical image modality, attack type, and defense methods
- Abstract
- Introduction
- Materials
- Methods
- Results
- Discussion
- Conclusions
- References
- Chapter 10. A deep ensemble network for lung segmentation with stochastic weighted averaging
- Abstract
- Introduction
- Related works
- Proposed system
- Results and discussion
- Conclusion
- References
- Chapter 11. Deep ensembles and data augmentation for semantic segmentation
- Abstract
- Introduction
- Methods
- Data augmentation
- Experimental results
- Conclusions
- Acknowledgment
- References
- Chapter 12. Classification of diseases from CT images using LSTM-based CNN
- Abstract
- Introduction
- Background
- CT dataset-issues and challenges in handling them
- Elucidating classical CNN- and LSTM-based CNN models
- Conclusion
- References
- Chapter 13. A novel polyp segmentation approach using U-net with saliency-like feature fusion
- Abstract
- Introduction
- Methodology
- Experiments and results
- Experimental results of enhanced images with image inpainting method
- Experimental results of proposed method
- Discussion
- Conclusion
- Compliance with ethical standards
- References
- Index
- No. of pages: 302
- Language: English
- Edition: 1
- Published: April 30, 2023
- Imprint: Academic Press
- Paperback ISBN: 9780323961295
- eBook ISBN: 9780323996815
KP
Kemal Polat
Professor Dr. Kemal Polat is a Professor in the Electrical and Electronic Engineering Department, Engineering of Faculty, Bolu Abant Izzet Baysal University, in Turkey. He has over 130 articles published in leading scientific journals and around 80 international conference papers. He is, amongst others, a member of the editorial board of the Journal of Neural Computing and Applications and editorial board member of Applied Soft Computing, Elsevier. His current research interests are in biomedical signal classification, control systems, electronics, statistical signal processing, visual memory, neuroscience, brain-computer interface, PPG signal, medical electronics, digital signal processing, pattern recognition, and classification.
Affiliations and expertise
Professor, Electrical and Electronic Engineering Department, Engineering of Faculty, Bolu Abant Izzet Baysal University, TurkeySÖ
Saban Öztürk
Dr. Şaban Öztürk is a researcher in Amasya University, in Turkey. He has over 50 scientific publications. Dr. Öztürk’s current research interests are in the fields of biomedical image processing, histopathological image analysis, hashing, content-based image retrieval, siamese networks and loss function, metric learning, deep learning, and image representation.
Affiliations and expertise
Researcher, Amasya University, TurkeyRead Diagnostic Biomedical Signal and Image Processing Applications With Deep Learning Methods on ScienceDirect