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Deep Learning in Healthcare - Sima Das - Bog

- Innovations, Applications & Future Aspect

Bag om Deep Learning in Healthcare

The intersection of deep learning and healthcare has ushered in a new era of possibilities, transforming the landscape of medical research, diagnosis, and treatment. This comprehensive volume explores the innovative applications of deep learning in various facets of healthcare, presenting a roadmap for the future of medical practice. The book begins by laying the foundation of deep learning principles and their adaptation to healthcare contexts. It delves into cutting-edge developments in medical imaging, showcasing how convolutional neural networks (CNNs) and recurrent neural networks (RNNs) are revolutionizing the accuracy and efficiency of diagnostic processes. From early detection of diseases to personalized treatment plans, the impact of deep learning on patient care is examined with real-world case studies. Beyond diagnostics, the book explores the integration of deep learning into electronic health records (EHRs) and clinical decision support systems, highlighting the potential to enhance data-driven decision-making in healthcare settings.

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  • Sprog:
  • Engelsk
  • ISBN:
  • 9786207455256
  • Indbinding:
  • Paperback
  • Udgivet:
  • 16. februar 2024
  • Størrelse:
  • 152x229x14 mm.
  • Vægt:
  • 363 g.
  • 2-3 uger.
  • 4. december 2024
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Prøv i 30 dage for 45 kr.
Herefter fra 79 kr./md. Ingen binding.

Beskrivelse af Deep Learning in Healthcare

The intersection of deep learning and healthcare has ushered in a new era of possibilities, transforming the landscape of medical research, diagnosis, and treatment. This comprehensive volume explores the innovative applications of deep learning in various facets of healthcare, presenting a roadmap for the future of medical practice. The book begins by laying the foundation of deep learning principles and their adaptation to healthcare contexts. It delves into cutting-edge developments in medical imaging, showcasing how convolutional neural networks (CNNs) and recurrent neural networks (RNNs) are revolutionizing the accuracy and efficiency of diagnostic processes. From early detection of diseases to personalized treatment plans, the impact of deep learning on patient care is examined with real-world case studies. Beyond diagnostics, the book explores the integration of deep learning into electronic health records (EHRs) and clinical decision support systems, highlighting the potential to enhance data-driven decision-making in healthcare settings.

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