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State of the Art in Neural Networks and Their Applications

Volume 2

Specificaties
Paperback, blz. | Engels
Elsevier Science | e druk, 2022
ISBN13: 9780128198728
Rubricering
Juridisch :
Elsevier Science e druk, 2022 9780128198728
€ 178,60
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Samenvatting

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.

Specificaties

ISBN13:9780128198728
Taal:Engels
Bindwijze:Paperback

Inhoudsopgave

1. Microscopy Cancer Cell Imaging in B-Lineage Acute Lymphoblastic Leukemia<br>2. Computational Applications in Brain and Breast Cancer <br>3. Deep Neural Networks and Advanced Computer Vision Algorithms in The Early Diagnosis of Skin Diseases<br>4. An Accurate Deep Learning-Based CAD System For Early Diagnosis Of Prostate Cancer<br>5. Adaptive Graph Convolutional Neural Network and its Biomedical Applications<br>6. Deep Slice Interpolation via Marginal Super-Resolution, Fusion and Refinement<br>7. New Explainable Deep CNN Design for Classifying Breast Tumor Response over Neoadjuvant Chemotherapy  <br>8. Deep Learning Interpretability: Measuring The Relevance of Clinical Concepts in CNN Features<br>9. Computational Lung Sound Classification: A Review<br>10. Clinical Applications of Machine Learning in Heart Failure<br>11. Role of AI and Radiomics in Diagnosing Renal Tumors: A Survey<br>12. Texture-Centric Diagnostic Models for Thyroid-Cancer Using Convolutional Neural Networks: Bridging the Gap Between Radiomics and Microscopic Domains

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        State of the Art in Neural Networks and Their Applications