Localization and classification of epiphyses in carpal radiographs using YOLO models

Authors

  • Guilherme Nique da Silva UFCSPA
  • Viviane Rodrigues Botelho UFCSPA
  • Thatiane Alves Pianoschi UFCSPA
  • Carla Diniz Lopes Becker UFCSPA

DOI:

https://doi.org/10.59681/2175-4411.v16.iEspecial.2024.1336

Keywords:

bone age, epiphysis, Object detection

Abstract

Objective: This study proposes the development of a model for detecting epiphyses in X-ray images using machine learning models. Methods: We describe the process of dataset acquisition and conduct tests with models such as YOLOv5, YOLOv8, and Faster R-CNN. Results: The YOLOv8 model achieved a 1% error rate on the DHA dataset, while the YOLOv5 model achieved around 5%. Conclusion: After a comparative analysis, YOLOv8 was selected as the ideal model for final epiphyses detection.

Author Biographies

Guilherme Nique da Silva, UFCSPA

Master’s Student, Federal University of Health Sciences of Porto Alegre – UFCSPA, Porto Alegre (RS), Brazil.

Viviane Rodrigues Botelho, UFCSPA

Ph.D., Federal University of Health Sciences of Porto Alegre – UFCSPA, DECESA, Porto Alegre (RS), Brazil.

Thatiane Alves Pianoschi, UFCSPA

Ph.D., Federal University of Health Sciences of Porto Alegre – UFCSPA, DECESA, Porto Alegre (RS), Brazil.

Carla Diniz Lopes Becker, UFCSPA

Ph.D., Federal University of Health Sciences of Porto Alegre – UFCSPA, DECESA, Porto Alegre (RS), Brazil.

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Published

2024-11-19

How to Cite

da Silva, G. N., Botelho, V. R., Pianoschi, T. A., & Becker, C. D. L. (2024). Localization and classification of epiphyses in carpal radiographs using YOLO models. Journal of Health Informatics, 16(Especial). https://doi.org/10.59681/2175-4411.v16.iEspecial.2024.1336

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