??Applications of Artificial Intelligence in Healthcare and Biomedicine provides ?updated knowledge on the applications of artificial intelligence in medical image analysis. The book starts with an introduction to Artificial Intelligence techniques for Healthcare and Biomedicine.
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 images and a deep learning-based approach to get a proper and faster diagnosis of the Optical Coherence Tomography (OCT) images. It also presents present 3D medical image analysis using 3D Convolutional Neural Networks (CNNs). Applications of Artificial Intelligence in Healthcare and Biomedicine closes with a chapter on 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.
1. Applications of Artificial Intelligence in Healthcare and Biomedicine - p. Page i 2. Cover - p. Page i 3. Front Matter - p. Page ii 4. Table of Contents - p. Page iii 5. Copyright - p. Page iv 6. Dedication - p. Page v 7. List of contributors - p. Page vi 8. Series preface - p. Page vi 9. Preface - p. Page vii 10. Acknowledgments - p. Page x 11. List of Illustrations - p. Page xi 12. List of Tables - p. Page xii 13. Chapter 1 : AI techniques for healthcare and biomedicine∗ - p. Page xii 14. Chapter 2 : Artificial intelligence-based emotion recognition using ECG signals - p. Page 34 15. Chapter 3 : Artificial intelligence–based depression detection using EEG signals - p. Page 66 16. Chapter 4 : Electromyography signal classification using artificial intelligence - p. Page 91 17. Chapter 5 : An evaluation of pretrained convolutional neural networks for stroke classification from brain CT images - p. Page 109 18. Chapter 6 : Brain tumor detection using deep learning from magnetic resonance images - p. Page 134 19. Chapter 7 : Artificial intelligence–based fatty liver disease detection using ultrasound images - p. Page 171 20. Chapter 8 : Deep learning approaches for breast cancer detection using breast MRI - p. Page 202 21. Chapter 9 : Automated detection of colon cancer from histopathological images using deep neural networks - p. Page 241 22. Chapter 10 : Optical coherence tomography image classification for retinal disease detection using artificial intelligence - p. Page 284 23. Chapter 11 : Heart muscles inflammation (myocarditis) detection using artificial intelligence - p. Page 322 24. Chapter 12 : Artificial intelligence for 3D medical image analysis - p. Page 354 25. Chapter 13 : Medical image segmentation using artificial intelligence - p. Page 375 26. Chapter 14 : DNA sequence classification using artificial intelligence - p. Page 399 27. Chapter 15 : Artificial intelligence in drug discovery and development - p. Page 415 28. Chapter 16 : Hospital readmission forecasting using artificial intelligence - p. Page 453 29. Index - p. Page 518
??Applications of Artificial Intelligence in Healthcare and Biomedicine provides ?updated knowledge on the applications of artificial intelligence in medical image analysis. The book starts with an introduction to Artificial Intelligence techniques for Healthcare and Biomedicine.
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 images and a deep learning-based approach to get a proper and faster diagnosis of the Optical Coherence Tomography (OCT) images. It also presents present 3D medical image analysis using 3D Convolutional Neural Networks (CNNs). Applications of Artificial Intelligence in Healthcare and Biomedicine closes with a chapter on 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.
1. Applications of Artificial Intelligence in Healthcare and Biomedicine - p. Page i 2. Cover - p. Page i 3. Front Matter - p. Page ii 4. Table of Contents - p. Page iii 5. Copyright - p. Page iv 6. Dedication - p. Page v 7. List of contributors - p. Page vi 8. Series preface - p. Page vi 9. Preface - p. Page vii 10. Acknowledgments - p. Page x 11. List of Illustrations - p. Page xi 12. List of Tables - p. Page xii 13. Chapter 1 : AI techniques for healthcare and biomedicine∗ - p. Page xii 14. Chapter 2 : Artificial intelligence-based emotion recognition using ECG signals - p. Page 34 15. Chapter 3 : Artificial intelligence–based depression detection using EEG signals - p. Page 66 16. Chapter 4 : Electromyography signal classification using artificial intelligence - p. Page 91 17. Chapter 5 : An evaluation of pretrained convolutional neural networks for stroke classification from brain CT images - p. Page 109 18. Chapter 6 : Brain tumor detection using deep learning from magnetic resonance images - p. Page 134 19. Chapter 7 : Artificial intelligence–based fatty liver disease detection using ultrasound images - p. Page 171 20. Chapter 8 : Deep learning approaches for breast cancer detection using breast MRI - p. Page 202 21. Chapter 9 : Automated detection of colon cancer from histopathological images using deep neural networks - p. Page 241 22. Chapter 10 : Optical coherence tomography image classification for retinal disease detection using artificial intelligence - p. Page 284 23. Chapter 11 : Heart muscles inflammation (myocarditis) detection using artificial intelligence - p. Page 322 24. Chapter 12 : Artificial intelligence for 3D medical image analysis - p. Page 354 25. Chapter 13 : Medical image segmentation using artificial intelligence - p. Page 375 26. Chapter 14 : DNA sequence classification using artificial intelligence - p. Page 399 27. Chapter 15 : Artificial intelligence in drug discovery and development - p. Page 415 28. Chapter 16 : Hospital readmission forecasting using artificial intelligence - p. Page 453 29. Index - p. Page 518
