Applications of Artificial Intelligence in Healthcare and Biomedicine

1st Edition - March 10, 2024
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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Applications 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.

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

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Abdulhamit Subasi