Enhanced multi-class object detector for bone fracture diagnosis with prescription recommendation
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Date
2026-01-12
Journal Title
Journal ISSN
Volume Title
Publisher
Frontiers
Abstract
Bone fractures are among the most prominent injuries in the modern world that
affect all ages and races. Traditional treatment involves radiographic imaging that
relies heavily on radiologists manually analyzing images. There have been efforts
to develop computer-aided diagnosis tools that employ artificial intelligence
and deep learning approaches. Existing literature focuses on developing tools
that only detect and classify bone fractures, rather than addressing the broader
issue of bone fracture management. However, evidence of scholarly works that
include treatment recommendations is still lacking. Furthermore, deep learning
based object detectors that achieve state-of-the-art results are computationally
expensive and considered as black-box solutions. Developing countries, such
as Sub-Saharan Africa, face a shortage of radiologists and orthopedists. For this
reason, this paper proposes a methodological approach that uses a more efficient
object detection model to diagnose long bone fractures and provide prescription
recommendations. An enhanced anchoring process, known as adaptive anchoring,
is proposed to improve the performance of the Regional Proposal Network and
the object detection model. A Faster R-CNN model with ResNet-50/101 and
ResNext-50/101 backbones was used to develop an object detection model that
uses X-ray images as input. To understand and interpret the model’s decision, a
Gradient-based Class Activation Mapping method was used to assess the model’s
learnability. The results indicate that the proposed adaptive anchoring approach can
improve computational efficiency, reducing training time by up to 29% compared
to the traditional approach. Model accuracy during training and validation ranged
between 94% and 98%. Overall, adaptive anchoring performed better when applied
with the ResNet-101 backbone, yielding an Average Precision of 92.73%, an F1
score of 96.01%, a precision of 96.80%, and a recall of 95.23%. The study provides
valuable insights into the use of computationally efficient deep learning models
for medical recommendation systems. Future studies should develop models to
diagnose fractures using input images from various modalities and to provide
prescription recommendations.
Sustainable Development Goals
SDG-3: Good Health and Well-Being
SDG-9: Industry, Innovation and Infrastructure
Keywords
Bone fracture, Object detection, Adaptive anchoring, Prescription, Tanzania