An Explainable ResNet-101 Framework for Hand Fracture Classification in X-ray
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Date
2026-04-23
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Co hosted by Baze University Big Data Analytics and Innovation Research Group, American University Yola, and Sule Lamido University
Abstract
: Fracture detection in radiographic images continues to be a difficult task due to
subtle bone disruptions and variations in anatomical structures, especially within the hand
region. This study proposes an explainable deep learning framework for automated hand
fracture detection using a ResNet-101 architecture. The model was trained and evaluated on
the hand subset of the publicly available MURA dataset, with preprocessing methods and data
balancing strategies applied to mitigate class imbalance and enhance model generalization.
Bayesian hyperparameter optimization was utilized to determine the most suitable training
parameters, including learning rate, dropout rate, and network capacity, thereby improving
model stability and overall performance. The optimized model achieved an accuracy of 79.7%
with an area under the curve (AUC) of 0.75, indicating dependable classification performance.
To enhance interpretability, Grad-CAM++ was incorporated to visualize the regions of interest
that influence the model’s predictions, allowing verification of clinically meaningful focus
areas. The results demonstrate that the model effectively detects fracture regions while
maintaining stable performance across diverse radiographic samples. In addition, the system
produces automated PDF-based clinical reports that combine predictions with corresponding
heatmaps, thereby improving its practical relevance in clinical environments. Overall, the
proposed approach offers a robust, interpretable, and clinically valuable solution for hand
fracture detection, with strong potential for integration into computer-aided diagnosis systems.
Future work will focus on extending the model to multi-centre datasets and incorporating
additional clinical features to further enhance generalization and diagnostic reliability
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10. Ayodeji Jubril Alabi, Shakirat Ronke Yusuff, Sulaiman Olaniyi Abdulsalam, Oyindamola Eniola Ajiboye (2026). An Explainable ResNet-101 Framework for Elbow Fracture Classification in X-ray Images. Journal of Institutional Research, Big Data Analytics and Innovation, 2(1). https://doi.org/10.5281/zenodo.19711312.