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Machine Learning Techniques for Medical Imaging

Bag om Machine Learning Techniques for Medical Imaging

Medical imaging or radiology is the field of medical science that involves the recreation of images of human body parts for the purpose of diagnosing and treating different medical conditions. There are various types of medical imaging procedures such as X-ray, magnetic resonance imaging (MRI), ultrasound, etc. The area of study within artificial intelligence which focuses on providing the computers with the capability to learn without being explicitly programmed is known as machine learning. Some radiological applications of machine learning include identification, portrayal and observation of infections, image classification, object detection, image segmentation and registration, image generation, and image transformation. Machine learning and artificial intelligence tools are used extensively for identifying cardiovascular abnormalities, neurological diseases, and detecting fractures and other musculoskeletal injuries. This book outlines the applications of machine learning in the field of medical imaging. It will prove to be immensely beneficial to medical students and researchers involved in this field.

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  • Sprog:
  • Engelsk
  • ISBN:
  • 9781646475339
  • Indbinding:
  • Hardback
  • Sideantal:
  • 251
  • Udgivet:
  • 26. September 2023
  • Størrelse:
  • 216x16x279 mm.
  • Vægt:
  • 875 g.
  • 2-3 uger.
  • 12. Oktober 2024
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Beskrivelse af Machine Learning Techniques for Medical Imaging

Medical imaging or radiology is the field of medical science that involves the recreation of images of human body parts for the purpose of diagnosing and treating different medical conditions. There are various types of medical imaging procedures such as X-ray, magnetic resonance imaging (MRI), ultrasound, etc. The area of study within artificial intelligence which focuses on providing the computers with the capability to learn without being explicitly programmed is known as machine learning. Some radiological applications of machine learning include identification, portrayal and observation of infections, image classification, object detection, image segmentation and registration, image generation, and image transformation. Machine learning and artificial intelligence tools are used extensively for identifying cardiovascular abnormalities, neurological diseases, and detecting fractures and other musculoskeletal injuries. This book outlines the applications of machine learning in the field of medical imaging. It will prove to be immensely beneficial to medical students and researchers involved in this field.

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