Albument-NAS: An Enhanced Bone Fracture Detection Model

Diagnosing fracture locations accurately is challenging, as it heavily depends on the radiologist's expertise; however, image quality, especially with minor fractures, can limit precision, highlighting the need for automated methods. The accuracy of diagnosing fracture locations often relies on radiologists' expertise; however, image quality, particularly with smaller fractures, can limit precision, underscoring the need for automated methods. Although a large volume of data is available for observation, many datasets lack annotated labels, and manually labeling this data would be highly time-consuming. This research introduces Albument-NAS, a technique that combines the One Shot Detector (OSD) model with the Albumentation image augmentation approach to enhance both speed and accuracy in detecting fracture locations. Albument-NAS achieved a mAP@50 of 83.5%, precision of 87%, and recall of 65.7%, significantly outperforming the previous state-of-the-art model, which had a mAP@50 of 63.8%, when tested on the GRAZPEDWRI dataset—a collection of pediatric wrist injury X-rays. These results establish a new benchmark in fracture detection, illustrating the advantages of combining augmentation techniques with advanced detection models to overcome challenges in medical image analysis.

@article{Fedora2024,
  title     = {Albument-NAS: An Enhanced Bone Fracture Detection Model},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {15},
  number    = {12},
  year      = {2024},
  publisher = {The Science and Information Organization},
  author    = {Evandiaz Fedora and Alexander Agung Santoso Gunawan},
  doi       = {10.14569/IJACSA.2024.0151221},
  url       = {https://doi.org/10.14569/IJACSA.2024.0151221}
}