State of the Art in Neural Networks and Their Applications, Volume Two presents the latest advances in artificial neural networks and their applications across a wide range of clinical diagnoses. The book provides over views and case studies of advances in the role of machine learning, artificial intelligence, deep learning, cognitive image processing, and suitable data analytics useful for clinical diagnosis and research applications. The application of neural network, artificial intelligence and machine learning methods in biomedical image analysis have resulted in the development of computer-aided diagnostic (CAD) systems that aim towards the automatic early detection of several severe diseases.
State of the Art in Neural Networks and Their Applications is presented in two volumes. Volume One: Neural Networks in Oncology Imaging covers lung cancer, prostate cancer, and bladder cancer. Volume Two: Neural Networks in Brain Disorders and Other Diseases covers autism spectrum disorder, Alzheimer’s disease, attention deficit hyperactivity disorder, hypertension, and other diseases. Written by experienced engineers in the field, these two volumes will help engineers, computer scientists, researchers, and clinicians understand the technology and applications of artificial neural networks.
1. State of the Art in Neural Networks and Their Applications - p. Page i 2. Cover - p. Page i 3. Title page - p. Page ii 4. Table of Contents - p. Page iii 5. Copyright - p. Page iii 6. Dedication - p. Page v 7. Contents - p. Page vii 8. List of contributors - p. Page viii 9. About the editors - p. Page viii 10. Acknowledgments - p. Page ix 11. List of Illustrations - p. Page xi 12. List of Tables - p. Page xii 13. Chapter 1 : Microscopy Cancer Cell Imaging in B-lineage Acute Lymphoblastic Leukemia - p. Page xii 14. Chapter 2 : Computational imaging applications in brain and breast cancer - p. Page 24 15. Chapter 3 : Deep neural networks and advanced computer vision algorithms in the early diagnosis of skin diseases - p. Page 40 16. Chapter 4 : An accurate deep learning-based computer-aided diagnosis system for early diagnosis of prostate cancer - p. Page 74 17. Chapter 5 : Adaptive graph convolutional neural network and its biomedical applications - p. Page 91 18. Chapter 6 : Deep slice interpolation via marginal super-resolution, fusion, and refinement - p. Page 129 19. Chapter 7 : Explainable deep learning approach to predict chemotherapy effect on breast tumor’s MRI - p. Page 144 20. Chapter 8 : Deep learning interpretability: measuring the relevance of clinical concepts in convolutional neural networks features - p. Page 154 21. Chapter 9 : Computational lung sound classification: a review - p. Page 188 22. Chapter 10 : Clinical applications of machine learning in heart failure - p. Page 209 23. Chapter 11 : Role of artificial intelligence and radiomics in diagnosing renal tumors: a survey - p. Page 226 24. Chapter 12 : A review of texture-centric diagnostic models for thyroid cancer using convolutional neural networks and visualized texture patterns - p. Page 246 25. Index - p. Page 281
State of the Art in Neural Networks and Their Applications, Volume Two presents the latest advances in artificial neural networks and their applications across a wide range of clinical diagnoses. The book provides over views and case studies of advances in the role of machine learning, artificial intelligence, deep learning, cognitive image processing, and suitable data analytics useful for clinical diagnosis and research applications. The application of neural network, artificial intelligence and machine learning methods in biomedical image analysis have resulted in the development of computer-aided diagnostic (CAD) systems that aim towards the automatic early detection of several severe diseases.
State of the Art in Neural Networks and Their Applications is presented in two volumes. Volume One: Neural Networks in Oncology Imaging covers lung cancer, prostate cancer, and bladder cancer. Volume Two: Neural Networks in Brain Disorders and Other Diseases covers autism spectrum disorder, Alzheimer’s disease, attention deficit hyperactivity disorder, hypertension, and other diseases. Written by experienced engineers in the field, these two volumes will help engineers, computer scientists, researchers, and clinicians understand the technology and applications of artificial neural networks.
1. State of the Art in Neural Networks and Their Applications - p. Page i 2. Cover - p. Page i 3. Title page - p. Page ii 4. Table of Contents - p. Page iii 5. Copyright - p. Page iii 6. Dedication - p. Page v 7. Contents - p. Page vii 8. List of contributors - p. Page viii 9. About the editors - p. Page viii 10. Acknowledgments - p. Page ix 11. List of Illustrations - p. Page xi 12. List of Tables - p. Page xii 13. Chapter 1 : Microscopy Cancer Cell Imaging in B-lineage Acute Lymphoblastic Leukemia - p. Page xii 14. Chapter 2 : Computational imaging applications in brain and breast cancer - p. Page 24 15. Chapter 3 : Deep neural networks and advanced computer vision algorithms in the early diagnosis of skin diseases - p. Page 40 16. Chapter 4 : An accurate deep learning-based computer-aided diagnosis system for early diagnosis of prostate cancer - p. Page 74 17. Chapter 5 : Adaptive graph convolutional neural network and its biomedical applications - p. Page 91 18. Chapter 6 : Deep slice interpolation via marginal super-resolution, fusion, and refinement - p. Page 129 19. Chapter 7 : Explainable deep learning approach to predict chemotherapy effect on breast tumor’s MRI - p. Page 144 20. Chapter 8 : Deep learning interpretability: measuring the relevance of clinical concepts in convolutional neural networks features - p. Page 154 21. Chapter 9 : Computational lung sound classification: a review - p. Page 188 22. Chapter 10 : Clinical applications of machine learning in heart failure - p. Page 209 23. Chapter 11 : Role of artificial intelligence and radiomics in diagnosing renal tumors: a survey - p. Page 226 24. Chapter 12 : A review of texture-centric diagnostic models for thyroid cancer using convolutional neural networks and visualized texture patterns - p. Page 246 25. Index - p. Page 281
