Computational Intelligence in Healthcare Applications discusses a variety of techniques designed to represent, enhance and empower inter-domain research based on computational intelligence in healthcare. The book serves as a reference for the pervasive healthcare domain which takes into consideration new convergent computing and other applications. The book discusses topics such as mathematical modeling in medical imaging, predictive modeling based on artificial intelligence and deep learning, smart healthcare and wearable devices, and evidence-based predictive modeling. In addition, it discusses computer-aided diagnostic for clinical inferences and pervasive and ubiquitous techniques in healthcare.
This book is a valuable resource for graduate students and researchers in medical informatics, however, it is also ideal for members of the biomedical field and healthcare industry who are interested in learning more about novel technologies and their applications in the field.
1. Title of Book - p. i 2. Cover image - p. i 3. Title page - p. ii 4. Table of Contents - p. iii 5. Copyright - p. iii 6. Contributors - p. ix 7. About the editors - p. xiii 8. Preface - p. xvii 9. Acknowledgments - p. xviii 10. Chapter 1. Clinical decision support systems: Benefits, potential challenges, and applications in pneumothorax segmentation - p. 1 11. Chapter 2. Opportunities and challenges for smart healthcare system in fog computing - p. 20 12. Chapter 3. Contemporary overview of bacterial vaginosis in conventional and complementary and alternative medicine - p. 48 13. Chapter 4. Computer-aided knee joint MR image segmentation—An overview - p. 55 14. Chapter 5. Computational approach to assess mucormycosis: A systematic review - p. 71 15. Chapter 6. A review of diabetes management tools and applications - p. 87 16. Chapter 7. Recent advancements of pelvic inflammatory disease: A review on evidence-based medicine - p. 116 17. A review of amenorrhea toward Unani to modern system with emerging technology: Current advancements, research gap, and future direction - p. 121 18. Chapter 9. Wearable EEG technology for the brain-computer interface - p. 137 19. Chapter 10. Automatic epileptic seizure detection based on the discrete wavelet transform approach using an artificial neural network classifier on the scalp electroencephalogram signal - p. 157 20. Chapter 11. Event identification by fusing EEG and EMG signals - p. 181 21. Chapter 12. Hand gesture recognition for the prediction of Alzheimer's disease - p. 187 22. Chapter 13. A frequency analysis-based apnea detection algorithm using photoplethysmography - p. 197 23. Chapter 14. Noninvasive health monitoring using bioelectrical impedance analysis - p. 209 24. Chapter 15. Detection of cancer from histopathology medical image data using ML with CNN ResNet-50 architecture - p. 237 25. Chapter 16. Performance analysis of augmented data for enhanced brain tumor image classification using transfer learning - p. 255 26. Chapter 17. Brain tumor detection through MRI using image thresholding, k-means, and watershed segmentation - p. 267 27. Chapter 18. An intelligent diagnostic technique using deep convolutional neural network - p. 285 28. Chapter 19. Design of a biosensor for the detection of glucose concentration in urine using 2D photonic crystals - p. 297 29. Chapter 20. Classification of pneumonic infections through chest radiography using textural features analysis and the pattern recognition system - p. 309 30. Chapter 21. Convolutional bi-directional long-short-term-memory based model to forecast COVID-19 in Algeria - p. 331 31. Index - p. 345
Computational Intelligence in Healthcare Applications discusses a variety of techniques designed to represent, enhance and empower inter-domain research based on computational intelligence in healthcare. The book serves as a reference for the pervasive healthcare domain which takes into consideration new convergent computing and other applications. The book discusses topics such as mathematical modeling in medical imaging, predictive modeling based on artificial intelligence and deep learning, smart healthcare and wearable devices, and evidence-based predictive modeling. In addition, it discusses computer-aided diagnostic for clinical inferences and pervasive and ubiquitous techniques in healthcare.
This book is a valuable resource for graduate students and researchers in medical informatics, however, it is also ideal for members of the biomedical field and healthcare industry who are interested in learning more about novel technologies and their applications in the field.
1. Title of Book - p. i 2. Cover image - p. i 3. Title page - p. ii 4. Table of Contents - p. iii 5. Copyright - p. iii 6. Contributors - p. ix 7. About the editors - p. xiii 8. Preface - p. xvii 9. Acknowledgments - p. xviii 10. Chapter 1. Clinical decision support systems: Benefits, potential challenges, and applications in pneumothorax segmentation - p. 1 11. Chapter 2. Opportunities and challenges for smart healthcare system in fog computing - p. 20 12. Chapter 3. Contemporary overview of bacterial vaginosis in conventional and complementary and alternative medicine - p. 48 13. Chapter 4. Computer-aided knee joint MR image segmentation—An overview - p. 55 14. Chapter 5. Computational approach to assess mucormycosis: A systematic review - p. 71 15. Chapter 6. A review of diabetes management tools and applications - p. 87 16. Chapter 7. Recent advancements of pelvic inflammatory disease: A review on evidence-based medicine - p. 116 17. A review of amenorrhea toward Unani to modern system with emerging technology: Current advancements, research gap, and future direction - p. 121 18. Chapter 9. Wearable EEG technology for the brain-computer interface - p. 137 19. Chapter 10. Automatic epileptic seizure detection based on the discrete wavelet transform approach using an artificial neural network classifier on the scalp electroencephalogram signal - p. 157 20. Chapter 11. Event identification by fusing EEG and EMG signals - p. 181 21. Chapter 12. Hand gesture recognition for the prediction of Alzheimer's disease - p. 187 22. Chapter 13. A frequency analysis-based apnea detection algorithm using photoplethysmography - p. 197 23. Chapter 14. Noninvasive health monitoring using bioelectrical impedance analysis - p. 209 24. Chapter 15. Detection of cancer from histopathology medical image data using ML with CNN ResNet-50 architecture - p. 237 25. Chapter 16. Performance analysis of augmented data for enhanced brain tumor image classification using transfer learning - p. 255 26. Chapter 17. Brain tumor detection through MRI using image thresholding, k-means, and watershed segmentation - p. 267 27. Chapter 18. An intelligent diagnostic technique using deep convolutional neural network - p. 285 28. Chapter 19. Design of a biosensor for the detection of glucose concentration in urine using 2D photonic crystals - p. 297 29. Chapter 20. Classification of pneumonic infections through chest radiography using textural features analysis and the pattern recognition system - p. 309 30. Chapter 21. Convolutional bi-directional long-short-term-memory based model to forecast COVID-19 in Algeria - p. 331 31. Index - p. 345
