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Machine Learning Methods in Biomedical Field: Computer-Aided Diagnostics, Healthcare and Biology Applications [Kõva köide]

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  • Formaat: Hardback, 460 pages, kõrgus x laius: 235x155 mm, 153 Illustrations, color; 18 Illustrations, black and white; XXX, 460 p. 171 illus., 153 illus. in color., 1 Hardback
  • Sari: Studies in Computational Intelligence 1218
  • Ilmumisaeg: 13-Sep-2025
  • Kirjastus: Springer International Publishing AG
  • ISBN-10: 303196327X
  • ISBN-13: 9783031963278
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  • Formaat: Hardback, 460 pages, kõrgus x laius: 235x155 mm, 153 Illustrations, color; 18 Illustrations, black and white; XXX, 460 p. 171 illus., 153 illus. in color., 1 Hardback
  • Sari: Studies in Computational Intelligence 1218
  • Ilmumisaeg: 13-Sep-2025
  • Kirjastus: Springer International Publishing AG
  • ISBN-10: 303196327X
  • ISBN-13: 9783031963278
Teised raamatud teemal:

This book provides an in-depth exploration of machine learning techniques and their biomedical applications, particularly in developing intelligent computer-aided diagnostic systems, creating groundbreaking healthcare technologies, uncovering novel biological applications, and fostering sustainable health solutions.
Integrating artificial intelligence, mathematical modeling, and emergent systems, this book highlights the profound impact of these advanced tools in not only enhancing problem-solving within the biomedical field but also in catalyzing the development of novel solutions.
This book is a valuable resource for readers interested in understanding the revolutionary impact of novel machine learning methodologies on the biomedical landscape. Furthermore, it offers a blend of theory and practical applications for those interested in biomedical education and training, biology, medicine, and sustainable health development.

Edge-enhanced Knowledge Distillation System for Diabetic Retinopathy
Lesions Computer-Aided Diagnosis.- Development of a Mobile Application for
Dermatological Diagnosis Using Image Recognition: The DermAware Case Study.-
Measuring the Diameter of Coronary Arteries via Skeletonization using a U-Net
Architecture.- Deep Belief Networks for Efficient Macular Edema Detection in
Retinal Fundus Images.- Automatic Spatial Localization of Coronary Stenosis
in X-ray Angiograms Using Deep Learning.- Deep Learning for Pediatric Right
Ventricle Segmentation in Echocardiography: Challenges and Strategies.-
Challenges and Advances in Digital Processing of Fetal Phonocardiography
Signal: A Review.- Implications of Model Loss and Configuration for Sparse
Histological Segmentation.- Metaheuristic Strategy in Automatic Robotics
Navigation for Patient Care in Hospitals.- Orthosis Control based on
Electromyographic Signals and Machine Learning.- Internet of Medical Things
Focused on Home Hospitalization for Diagnostic and Monitoring Support.-
Automatic Robotics Medication Delivery System: The ANDIS Case Study.- Making
Better Medical Decisions Using Machine Learning: A Bayesian Model.-
Determining the Influence of Socioeconomic and Clinical Factors in Diabetes
in the Mexican Population Using Machine Learning Techniques.- Sphonic:
Development of a Mobile Application Using AI and AR for Learning Biomedical
Concepts.- A Case Study on Pigmentation of Marine Species in Captivity and a
Possible Application of AI to Marine Biomedical Research.- Ligand-based
Virtual Screening Workflow for Antimalarial Repositioning from Known Drugs
and Chemical Libraries.- Redefining Care: Hospitals Pivotal Role in
Sustainable Development.- Cutting-Edge Technologies: Driving Sustainability
in Hospital Operations.