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Bone age recognition based on mask R-CNN using xception regression model.

Zhi-Qiang Liu1, Zi-Jian Hu2, Tian-Qiong Wu1

  • 1Department of Radiology, Guangzhou Twelfth People's Hospital, Guangzhou, China.

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Summary

This study introduces an AI model for accurate bone age assessment using Mask R-CNN for hand bone segmentation and Xception for regression. The AI achieves high accuracy, improving upon traditional methods for medical and forensic applications.

Keywords:
Xceptionbone age assessmentdeep learninghand bone X-ray imagesmask R-CNN

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Computer Vision

Background:

  • Traditional bone age assessment relies on manual interpretation of hand X-rays, which is subjective and prone to errors.
  • Machine learning and neural networks offer potential for more objective and accurate bone age detection.
  • Computer-aided detection enhances diagnostic validity in fields like medicine, sports, and forensics.

Purpose of the Study:

  • To develop an automated system for precise bone age assessment.
  • To improve the accuracy and reliability of bone age detection compared to manual methods.
  • To leverage deep learning for efficient and robust bone age evaluation.

Main Methods:

  • A Mask R-CNN based network was employed for accurate segmentation of hand bone regions.
  • The segmented hand bone images were fed into an enhanced Xception regression network for bone age prediction.
  • A convolutional block attention module was integrated to refine feature mapping for improved accuracy.

Main Results:

  • The Mask R-CNN model achieved a high Dice coefficient of 0.976 for hand bone segmentation.
  • The proposed system demonstrated a low mean absolute error of 4.97 months in bone age prediction.
  • The AI model effectively eliminated background noise, improving the focus on relevant bone structures.

Conclusions:

  • The combined Mask R-CNN and Xception model significantly enhances bone age assessment accuracy.
  • This AI-driven approach offers a robust and accurate solution for clinical bone age evaluation.
  • The system shows promise for widespread application in medical, sports, and judicial settings.