FEC-IGE: An Efficient Approach to Classify Fracture Based on Convolutional Neural Networks and Integrated Gradients Explanation

In this paper, we propose the FEC-IGE framework includes data preprocessing, data augmentation, transfer learning, and fine-tuning of the pre-trained model of convolutional neural network (CNN) architecture for the problem of bone fracture classification. Bone fractures are a widespread medical issue globally, with a significant prevalence and imposing substantial burdens on individuals and healthcare systems. The impact of bone fractures extends beyond physical injury, often leading to pain, reduced mobility, and decreased quality of life for affected individuals. Moreover, fractures can incur substantial economic costs due to medical expenses, rehabilitation, and lost productivity. In recent years, progress in machine learning methodologies has exhibited potential in tackling issues pertaining to fracture diagnosis and classification. By harnessing the capabilities of deep learning frameworks, scholars aspire to design precise and effective mechanisms for automatically detecting and classifying bone fractures from medical imaging data. In this study, FEC-IGE framework has demonstrated its potential and strength when applied models pre-trained of CNN architecture in the task of classifying X-ray bone fracture images with accuracies of 98.48%, 96.92%, and 97.24% in three experimental scenarios. These outcomes are the consequence of the model’s fine-tuning and transfer learning procedures applied to an enhanced dataset including 1129 X-ray pictures classified into ten different kinds of fractures: avulsion fracture, comminuted fracture, fracture dislocation, greenstick fracture, hairline fracture, impacted fracture, longitudinal fracture, oblique fracture, pathological fracture, and spiral fracture. To increase transparency and understanding of the model, Integrated Gradients explanation was also applied in this study. Finally, metrics including precision, recall, F1 score, precision, and confusion matrix were applied to evaluate performance and other in-depth analysis.

@article{Nguyen2024,
  title     = {FEC-IGE: An Efficient Approach to Classify Fracture Based on Convolutional Neural Networks and Integrated Gradients Explanation},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {15},
  number    = {6},
  year      = {2024},
  publisher = {The Science and Information Organization},
  author    = {Triet Minh Nguyen and Thuan Van Tran and Quy Thanh Lu},
  doi       = {10.14569/IJACSA.2024.01506142},
  url       = {https://doi.org/10.14569/IJACSA.2024.01506142}
}