Enhancing Bone Fracture Detection: A Feature-Based Transfer Learning Approach Using DenseNet with SVM

Accurate classification of bone fractures is essential for effective treatment, yet traditional methods can be error-prone and time-consuming. Transfer learning, particularly feature-based transfer learning, has shown promise in improving medical image analysis. This study investigates the effectiveness of DenseNet architectures combined with SVM classifiers with default hy-perparameter for classifying hairline and pathological fractures. A dataset of 500 bone fracture x-ray images was utilized from open source for this study. Features were extracted using DenseNet121, DenseNet169, and Dense-Net201 models. The findings indicated that the combination of DenseNet169 and SVM yielded the highest overall accuracy and generalization capacity, with a notable proficiency in differentiating hairline fractures. The classifica-tion performance of DenseNet121-SVM and DenseNet201-SVM was robust, although significantly less successful in identifying hairline fractures. The exceptional performance of the DenseNet169-SVM model highlights its promise as a dependable tool for automated bone fracture classification in medical imaging. This work emphasizes the efficacy of integrating advanced transfer learning techniques with conventional classifiers to enhance diag-nostic precision, hence facilitating improvements in medical procedures. Ad-ditional investigation is advised to authenticate these discoveries using more extensive, practical clinical datasets.