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.
- Includes applications of neural networks, AI, machine learning, and deep learning techniques to a variety of oncology imaging technologies
- Provides in-depth technical coverage of computer-aided diagnosis (CAD), including coverage of computer-aided classification, unified deep learning frameworks, 3D MRI, PET/CT, and more
- Covers deep learning cancer identification from histopathological images, medical image analysis, detection, segmentation and classification via AI
Sprache
Verlagsort
Verlagsgruppe
Elsevier Science & Techn.
Dateigröße
ISBN-13
978-0-12-819912-1 (9780128199121)
Schweitzer Klassifikation
1. Microscopy Cancer Cell Imaging in B-Lineage Acute Lymphoblastic Leukemia2. Computational Applications in Brain and Breast Cancer 3. Deep Neural Networks and Advanced Computer Vision Algorithms in The Early Diagnosis of Skin Diseases4. An Accurate Deep Learning-Based CAD System For Early Diagnosis Of Prostate Cancer5. Adaptive Graph Convolutional Neural Network and its Biomedical Applications6. Deep Slice Interpolation via Marginal Super-Resolution, Fusion and Refinement7. New Explainable Deep CNN Design for Classifying Breast Tumor Response over Neoadjuvant Chemotherapy 8. Deep Learning Interpretability: Measuring The Relevance of Clinical Concepts in CNN Features9. Computational Lung Sound Classification: A Review10. Clinical Applications of Machine Learning in Heart Failure11. Role of AI and Radiomics in Diagnosing Renal Tumors: A Survey12. Texture-Centric Diagnostic Models for Thyroid-Cancer Using Convolutional Neural Networks: Bridging the Gap Between Radiomics and Microscopic Domains
