Abstract: 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.

Evandiaz Fedora and Alexander Agung Santoso Gunawan, “Albument-NAS: An Enhanced Bone Fracture Detection Model” International Journal of Advanced Computer Science and Applications(IJACSA), 15(12), 2024. http://dx.doi.org/10.14569/IJACSA.2024.0151221

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