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Adaptive kernel selection network with attention constraint for surgical instrument classification.

Yaqing Hou1, Wenkai Zhang1, Qian Liu1

  • 1School of Computer Science and Technology, Dalian University of Technology, Dalian, China.

Neural Computing & Applications
|September 20, 2021
PubMed
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This study introduces SKA-ResNet, a novel deep learning model for surgical instrument classification using computer vision. It achieves 97.703% accuracy, enhancing surgical tool inventory and recognition.

Area of Science:

  • Medical Technology
  • Computer Vision
  • Artificial Intelligence

Background:

  • Computer vision (CV) aids healthcare, but surgical instrument inventory lacks CV research.
  • Accurate inventory is crucial for preventing surgical tool loss and ensuring patient safety.

Purpose of the Study:

  • To develop a systematic classification method for surgical instruments using CV.
  • To introduce a novel attention-based deep neural network (SKA-ResNet) for this task.

Main Methods:

  • Developed SKA-ResNet with a selective kernel attention feature extractor and a multi-scale regularizer.
  • Created the SID19 dataset, comprising 3800 images of 19 surgical instrument types.
  • Trained the model end-to-end in a single stage with minimal computational overhead.
Keywords:
Attention mechanismDeep learningFine-grained classificationHealth care

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Main Results:

  • SKA-ResNet achieved 97.703% classification accuracy on the SID19 dataset.
  • Demonstrated superior performance compared to state-of-the-art models in surgical tool classification.
  • Attained state-of-the-art results on four fine-grained visual classification datasets.

Conclusions:

  • SKA-ResNet effectively classifies surgical instruments, supporting inventory and recognition processes.
  • The proposed method offers a robust and efficient solution for surgical tool management.
  • This work advances the application of computer vision in surgical environments.