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Computer Vision-Based Medical Cloud Data System for Back Muscle Image Detection.

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Area of Science:

  • Medical Imaging
  • Computer Vision
  • Health Informatics

Background:

  • Increasing workload for healthcare professionals due to doctor-patient ratios.
  • Need for efficient medical image analysis in diagnostics.
  • Potential of artificial intelligence in healthcare applications.

Purpose of the Study:

  • To design and implement a cloud-based medical image recognition system.
  • To alleviate doctor workload in analyzing CT and ultrasound images.
  • To improve remote doctor-patient communication and interaction efficiency.

Main Methods:

  • Utilized semantic segmentation for CT and ultrasound images.
  • Employed convolutional neural networks (CNNs) for feature extraction and classification.
  • Developed a cloud platform for medical image processing and analysis.

Main Results:

  • Achieved accurate detection of back muscles using the cloud platform and CNN algorithm.
  • The system demonstrated stable performance and rapid image segmentation within the required range.
  • The developed algorithm partially met the proposed accuracy requirements.

Conclusions:

  • The medical image recognition system effectively assists medical workers and patients.
  • The system shows potential for exploring the effects of muscle activity on the lumbar region.
  • Further refinement of the algorithm is needed to fully meet accuracy requirements.