Obálka

Machine Learning and Deep Learning Modeling and Algorithms with Applications in Medical and Health Care

This book explains medical image processing and analysis using deep learning algorithms to analyze medical data. It focuses on the latest achievements and developments in applying this analysis to medical imaging, clinical, and other healthcare applications. The book covers among other areas: Image...

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Korporativní autor: SpringerLink (Online service)
Další autoři: Diwakar, Manoj (Editor), Ravi, Vinayakumar (Editor), Singh, Prabhishek (Editor), Pham, Hoang (Editor)
Médium: Elektronický zdroj E-kniha
Jazyk:angličtina
Vydáno: Cham : Springer Nature Switzerland : Imprint: Springer, 2025.
Vydání:1st ed. 2025.
Edice:Springer Series in Reliability Engineering,
Témata:
Medical physics.
Machine learning.
Algorithms.
Medical care.
Industrial engineering.
Production engineering.
Operations research.
Medical Physics.
Machine Learning.
Health Care.
Industrial and Production Engineering.
Operations Research and Decision Theory.
On-line přístup:https://doi.org/10.1007/978-3-031-98728-1
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Obsah:
  • Enhancing dysarthric speech for improved clinical communication: A deep learning approach
  • Speech-based real-world scene understanding for assistive care of the visually impaired
  • Medical image segmentation with deep learning: An overview
  • Lightweight generative model for synthetic biomedical images with enhanced quality
  • Pediatric dental disease detection using X-ray image enhancements and deep learning algorithms
  • Evaluation of Parkinson disease from MRI images using deep learning techniques
  • Analyzing the effect of eyes open and eyes closed states on EEG in Parkinson’s disease with ON and OFF medication
  • Automated detection of diabetic retinopathy using ResNet-50 deep learning model
  • Deep learning model for decoding subcortical brain activity from simultaneous EEG-FMRI multi-model data
  • Secure transmission of medical images in IoMT for smart cities using data hiding scheme
  • Deep learning approaches to heart stroke prediction: Model evaluation and insights
  • Harnessing predictive modeling techniques for early detection and management of diseases: Challenges, innovations, and future directions
  • Fundamentals of machine learning and deep learning for healthcare applications
  • Automatic detection of Parkinson disease through various machine learning models
  • Transforming healthcare: The role of AI and ML in disease prediction, treatment, and patient satisfaction
  • Multi-modality medical (CT, MRI, ultrasound etc.) Image fusion using machine learning/deep learning
  • Leveraging digital devices for objective behavioral health assessment: Computational machine learning methods for sleep and mental health evaluation
  • Optimizing medical image quality through hybrid machine learning techniques and convolutional denoising autoencoders
  • Image segmentation in multimodal medical imaging using deep learning models
  • Brain MRI analysis for multiple sclerosis detection using deep learning techniques.

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