Tripti Goel,
M. Tanveer,
Amir Hussain,
Zain Hussain
e.a.
Deep Learning Assessment of Neurological Imaging
Paperback Engels 2026 9780443302916Levertijd ongeveer 9 werkdagen
Gratis verzonden
Samenvatting
Deep Learning Assessment of Neurological Imaging provides an introduction to deep learning structures and pre-processing methods for detecting MRI anomalies. The book also provides a comprehensive accounting of deep learning research on MRI images for Alzheimer's disease, Parkinson's disease, and schizophrenia, and includes a discussion on current research issues and future objectives. This book is a valuable resource to guide new entrants in the field, helping them make a meaningful impact in their development efforts. The book concludes with a brief overview of problems and potential future advancements in the field.
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Inhoudsopgave
1. Graph convolutional networks for diagnosis of Alzheimer’s disease: A review
2. Introduction to Deep Learning Algorithms
3. Application of Deep Learning for Brain Tumors.
4. Application of Deep Learning for Parkinson’s Disease.
5. Predicting Biological Age of the Brain using Magnetic Resonance Imaging and Deep Learning Methods
6. Artificial Intelligence in Schizophrenia Detection: A Comparative Review of Machine Learning and Deep Learning Approaches
7. Ensemble of deep learning Algorithms for neurological disorders
8. A Framework for Characterizing Brain Iron Concentration in Neurodegenerative disease using Quantitative Susceptibility Mapping
9. Application of Deep Learning for Brain Stroke Diagnosis
10. Advanced Entropy Feature Engineering Using EMD and VMD for Schizophrenia Classification
11. Issues and Challenges of Neurological Disorders Diagnosis Research
12. Opportunities and Future Directions of Neurological Disorders Diagnosis Research
13. Neurology beyond Deep Learning
2. Introduction to Deep Learning Algorithms
3. Application of Deep Learning for Brain Tumors.
4. Application of Deep Learning for Parkinson’s Disease.
5. Predicting Biological Age of the Brain using Magnetic Resonance Imaging and Deep Learning Methods
6. Artificial Intelligence in Schizophrenia Detection: A Comparative Review of Machine Learning and Deep Learning Approaches
7. Ensemble of deep learning Algorithms for neurological disorders
8. A Framework for Characterizing Brain Iron Concentration in Neurodegenerative disease using Quantitative Susceptibility Mapping
9. Application of Deep Learning for Brain Stroke Diagnosis
10. Advanced Entropy Feature Engineering Using EMD and VMD for Schizophrenia Classification
11. Issues and Challenges of Neurological Disorders Diagnosis Research
12. Opportunities and Future Directions of Neurological Disorders Diagnosis Research
13. Neurology beyond Deep Learning
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