Applications of Artificial Intelligence in Healthcare and Biomedicine
- 1st Edition - March 10, 2024
- Imprint: Academic Press
- Author: Abdulhamit Subasi
- Language: English
- Paperback ISBN:9 7 8 - 0 - 4 4 3 - 2 2 3 0 8 - 2
- eBook ISBN:9 7 8 - 0 - 4 4 3 - 2 2 3 0 9 - 9
Applications of Artificial Intelligence in Healthcare and Biomedicine provides updated knowledge on the applications of artificial intelligence in medical image analysis.… Read more
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Request a sales quoteApplications of Artificial Intelligence in Healthcare and Biomedicine provides updated knowledge on the applications of artificial intelligence in medical image analysis. In 16 chapters, it presents artificial applications in Electrocardiogram (ECG), Electroencephalogram (EEG) and Electromyography (EMG), signal analysis, Computed Tomography (CT), Magnetic Resonance Imaging (MR), and Ultrasound image analysis. It equips researchers with tools for early breast cancer detection from mammograms using artificial intelligence (AI), AI models to detect lung cancer using histopathological image, and a deep learning-based approach to get a proper and faster diagnosis of the Optical Coherence Tomography (OCT) images.
In addition, it presents 3D medical image analysis using 3D Convolutional Neural Networks (CNNs). Final sections cover an AI-based approach to forecast diabetes patients' hospital re-admissions. This is a valuable resource for clinicians, researchers, and healthcare professionals who are interested in learning more about the applications of Artificial Intelligence and its impact in medical/biomedical image analysis.
- Provides knowledge on Artificial Intelligence algorithms for clinical data analysis
- Gives insights into both AI applications in biomedical signal analysis, biomedical image analysis, and applications in healthcare, including drug discovery
- Equips researchers with tools for early breast cancer detection
Researchers, professionals, academicians, and graduate students on medical informatics. Researchers, professionals, academicians, and graduate students on computer science
- Cover image
- Title page
- Table of Contents
- Copyright
- Dedication
- List of contributors
- Series preface
- Preface
- Acknowledgments
- Chapter 1. AI techniques for healthcare and biomedicine
- 1. Introduction
- 2. Unsupervised learning (clustering)
- 3. Supervised learning
- Chapter 2. Artificial intelligence-based emotion recognition using ECG signals
- 1. Introduction
- 2. Literature review
- 3. Atrificial intelligence based emotion recognotion
- 4. Conclusion
- Chapter 3. Artificial intelligence–based depression detection using EEG signals
- 1. Introduction
- 2. Background/literature review
- 3. Artificial intelligence for depression detection
- 4. Result and discussion
- 5. Conclusion
- Chapter 4. Electromyography signal classification using artificial intelligence
- 1. Introduction
- 2. Literature review
- 3. Artificial intelligence for diagnosis of neuromuscular disorders
- 4. Discussion
- 5. Summary
- Chapter 5. An evaluation of pretrained convolutional neural networks for stroke classification from brain CT images
- 1. Introduction
- 2. Related work
- 3. Methodology
- 4. Dataset
- 5. Experimental results
- 6. Conclusion
- Chapter 6. Brain tumor detection using deep learning from magnetic resonance images
- 1. Introduction
- 2. Background/literature review
- 3. Artificial intelligence for brain tumors detection
- 4. Results and discussion
- 5. Conclusion
- Chapter 7. Artificial intelligence–based fatty liver disease detection using ultrasound images
- 1. Introduction
- 2. Literature review
- 3. Dataset
- 4. Proposed architecture
- 5. NAFLD detection with artificial intelligence
- 6. Results and discussions
- 7. Discussion
- 8. Conclusion
- Chapter 8. Deep learning approaches for breast cancer detection using breast MRI
- 1. Introduction
- 2. Literature review
- 3. Subjects and data acquisition
- 4. Proposed architecture for custom convolutional neural networks
- 5. Proposed pipeline for transfer learning
- 6. Breast cancer detection with deep feature extraction
- 7. Results and discussions
- 8. Conclusion
- Chapter 9. Automated detection of colon cancer from histopathological images using deep neural networks
- 1. Introduction
- 2. Background/literature review
- 3. Machine learning techniques
- 4. Results and discussion
- Chapter 10. Optical coherence tomography image classification for retinal disease detection using artificial intelligence
- 1. Introduction
- 2. Related work
- 3. Dataset
- 4. Implementation details
- 5. Results and discussions
- 6. Discussion
- 7. Conclusion
- Chapter 11. Heart muscles inflammation (myocarditis) detection using artificial intelligence
- 1. Introduction
- 2. Literature review
- 3. Subjects and data acquisition
- 4. Proposed architecture for custom convolutional neural networks
- 5. Proposed pipeline for transfer learning
- 6. Detection of myocarditis with deep feature extraction
- 7. Results and discussions
- 8. Conclusion
- Chapter 12. Artificial intelligence for 3D medical image analysis
- 1. Introduction
- 2. Literature review
- 3. Artificial intelligence for 3D image classification
- 4. Discussion
- 5. Conclusion
- Chapter 13. Medical image segmentation using artificial intelligence
- 1. Introduction
- 2. Background and literature review
- 3. Deep learning methods for biomedical image segmentation
- 4. Medical image segmentation with TransResUNet
- 5. Discussion
- 6. Conclusion
- Chapter 14. DNA sequence classification using artificial intelligence
- 1. Introduction
- 2. Literature review
- 3. DNA sequencing with machine learning
- 4. Results
- 5. Discussion
- 6. Conclusion
- Chapter 15. Artificial intelligence in drug discovery and development
- 1. Introduction
- 2. Background and literature review
- 3. Artificial intelligence for DDD
- 4. Discussion
- 5. Conclusion
- Chapter 16. Hospital readmission forecasting using artificial intelligence
- 1. Introduction
- 2. Background and literature review
- 3. Artificial intelligence for hospital readmission forecasting
- 4. Discussion
- 5. Conclusion
- Index
- Edition: 1
- Published: March 10, 2024
- Imprint: Academic Press
- No. of pages: 548
- Language: English
- Paperback ISBN: 9780443223082
- eBook ISBN: 9780443223099
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Abdulhamit Subasi
Abdulhamit Subasi is a highly specialized expert in the fields of Artificial Intelligence, Machine Learning, and Biomedical Signal and Image Processing. His extensive expertise in applying machine learning across diverse domains is evident in his numerous contributions, including the authorship of multiple book chapters, as well as the publication of a substantial body of research in esteemed journals and conferences. His career has spanned various prestigious institutions, including the Georgia Institute of Technology in Georgia, USA, where he served as a dedicated researcher. In recognition of his outstanding research contributions, Subasi received the prestigious Queen Effat Award for Excellence in Research in May 2018. His academic journey includes a tenure as a Professor of computer science at Effat University in Jeddah, Saudi Arabia, from 2015 to 2020. Since 2020, he has assumed the role of Professor of medical physics at the Faculty of Medicine, University of Turku in Turku, Finland
Affiliations and expertise
Institute of Biomedicine, Faculty of Medicine, University of Turku, Turku, Finland Department of Computer Science, College of Engineering, Effat University, Jeddah, Saudi ArabiaRead Applications of Artificial Intelligence in Healthcare and Biomedicine on ScienceDirect