Cutting-edge Computational Intelligence in Healthcare with Convolution and Kronecker Convolution-based Approaches focuses on the use of deep learning techniques in the field of medical imagine analysis. These advances offer promising progress in healthcare through improvements in diagnostic accuracy, efficiency in medical image interpretation, and breakthroughs in treatment planning. Divided into five sections, the book begins with foundational coverage of deep learning in medical imaging and fundamentals of Convolutional Neural Networks. Discover the role convolutions play in extracting meaningful features from images, aiding tasks such as diagnosis and segmentation. The second section takes a deep dive into Kronecker convolutions and their unique advantages, such as enhanced spatial hierarchy understanding, efficient parameter utilization, and improved adaptability to specific characteristics of medical images. Section three reviews specific applications in tumor detection, enhancing organ segmentation as well as disease classification, and section four explores real-world implementation of AI-driven diagnostic imaging, precision medicine via imaging analytics, and wearable devices and continuous health monitoring. The final section offers discussion on the unique challenges, trends, and potential future directions these innovative computational approaches have on medical image processing and advanced healthcare. In summary, this book takes an interdisciplinary approach to bridge the gap between theory and practice, fusing knowledge from the domains of medicine, computer science, and machine learning to address issues in healthcare through sophisticated image analysis techniques.
1. Chapter 1. Introduction to deep learning in medical imaging - p. 3 2. Chapter 2. Fundamentals of convolutional neural networks - p. 21 3. Section 2: Advanced techniques in deep learning with kronecker convolutions - p. S41 4. Chapter 3. Kronecker convolutions ensemble vision transformer and 3D kronecker U-net for volumetric segmentation of kidney stones, cysts and tumor from CT scans - p. 43 5. Chapter 4. Image processing techniques in healthcare for early detection of heart diseases - p. 63 6. Section 3: Applications in medical imaging - p. S81 7. Chapter 5. Automated atypical teratoid /rhabdoid tumor detection in magnetic resonance imaging usingdeep learning - p. 83 8. Chapter 6. Ischemic stroke lesion segmentation using multiscale processing and knowledge distillation through intra-domain teacher - p. 95 9. Chapter 7. Disease classification through advanced neural networks - p. 117 10. Section 4: Real-world implementation - p. S137 11. Chapter 8. GAT-Net: ghost attention network for classification of gait-based neurodegenerative diseases - p. 139 12. Chapter 9. Artificial intelligence-enhanced diagnostics: deep learning in medical imaging - p. 149 13. Chapter 10. Precision medicine through imaging analytics: Kronecker convolutions in tumor detection - p. 171 14. Chapter 11. Diagnosis of schizophrenia using convolutional neural networks based on multichannel electroencephalography signal - p. 183 15. Chapter 12. Detection of anomalies in physiological signals using artificial neural network - p. 215 16. Chapter 13. Advancements in electrocardiography-based detection of obstructive sleep apnea: a deep learning approach - p. 239 17. Chapter 14. Machine learning-based life expectancy post chest surgery - p. 249 18. Section 5: Future directions and conclusion - p. S261 19. Chapter 15. Challenges and future directions in medical image analysis - p. 263 20. Index - p. 295
Cutting-edge Computational Intelligence in Healthcare with Convolution and Kronecker Convolution-based Approaches focuses on the use of deep learning techniques in the field of medical imagine analysis. These advances offer promising progress in healthcare through improvements in diagnostic accuracy, efficiency in medical image interpretation, and breakthroughs in treatment planning. Divided into five sections, the book begins with foundational coverage of deep learning in medical imaging and fundamentals of Convolutional Neural Networks. Discover the role convolutions play in extracting meaningful features from images, aiding tasks such as diagnosis and segmentation. The second section takes a deep dive into Kronecker convolutions and their unique advantages, such as enhanced spatial hierarchy understanding, efficient parameter utilization, and improved adaptability to specific characteristics of medical images. Section three reviews specific applications in tumor detection, enhancing organ segmentation as well as disease classification, and section four explores real-world implementation of AI-driven diagnostic imaging, precision medicine via imaging analytics, and wearable devices and continuous health monitoring. The final section offers discussion on the unique challenges, trends, and potential future directions these innovative computational approaches have on medical image processing and advanced healthcare. In summary, this book takes an interdisciplinary approach to bridge the gap between theory and practice, fusing knowledge from the domains of medicine, computer science, and machine learning to address issues in healthcare through sophisticated image analysis techniques.
1. Chapter 1. Introduction to deep learning in medical imaging - p. 3 2. Chapter 2. Fundamentals of convolutional neural networks - p. 21 3. Section 2: Advanced techniques in deep learning with kronecker convolutions - p. S41 4. Chapter 3. Kronecker convolutions ensemble vision transformer and 3D kronecker U-net for volumetric segmentation of kidney stones, cysts and tumor from CT scans - p. 43 5. Chapter 4. Image processing techniques in healthcare for early detection of heart diseases - p. 63 6. Section 3: Applications in medical imaging - p. S81 7. Chapter 5. Automated atypical teratoid /rhabdoid tumor detection in magnetic resonance imaging usingdeep learning - p. 83 8. Chapter 6. Ischemic stroke lesion segmentation using multiscale processing and knowledge distillation through intra-domain teacher - p. 95 9. Chapter 7. Disease classification through advanced neural networks - p. 117 10. Section 4: Real-world implementation - p. S137 11. Chapter 8. GAT-Net: ghost attention network for classification of gait-based neurodegenerative diseases - p. 139 12. Chapter 9. Artificial intelligence-enhanced diagnostics: deep learning in medical imaging - p. 149 13. Chapter 10. Precision medicine through imaging analytics: Kronecker convolutions in tumor detection - p. 171 14. Chapter 11. Diagnosis of schizophrenia using convolutional neural networks based on multichannel electroencephalography signal - p. 183 15. Chapter 12. Detection of anomalies in physiological signals using artificial neural network - p. 215 16. Chapter 13. Advancements in electrocardiography-based detection of obstructive sleep apnea: a deep learning approach - p. 239 17. Chapter 14. Machine learning-based life expectancy post chest surgery - p. 249 18. Section 5: Future directions and conclusion - p. S261 19. Chapter 15. Challenges and future directions in medical image analysis - p. 263 20. Index - p. 295
