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https://doi.org/10.1007/978-3-030-58452 8_13 Darabi, P. K. (2024). Vehicle detection image dataset: Suitable for object detection, tracking tasks [Data set]. Kaggle. https://www.kaggle.com/datasets/pkdarabi /vehicle-detection-image-dataset/ Gao, L., Zhang, L., Liu, C., & Wu, S. (2020). Handling imbalanced medical image data: A deep-learning-based one-class classification approach. Artificial Intelligence in Medicine , 108 , 101935. https://doi.org/10.1016/j.artmed.2020.101 935 He, K., Girshick, R., & Dollár, P. (2019). Rethinking ImageNet pre-training. Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) , 4918–4927. https://doi.org/10.1109/ICCV.2019.00502 He, L., Zhou, Y., Liu, L., & Ma, J. (2024). Research and applications of YOLOv11-based object segmentation in intelligent recognition at construction sites. Buildings , 14 (12), 3777. https://doi.org/10.3390/buildings1412377 7 Jeong, S., & Lee, B. J. (2025). Advancing spine fracture detection: The role of artificial intelligence in clinical practice. Korean Journal of Neurotrauma , 21 (3), 172-182. https://doi.org/10.13004/kjnt.2025.21.e22 Khalifa, M., & Albadawy, M. (2024). AI in diagnostic imaging: Revolutionising accuracy and efficiency. Computer Methods and Programs in Biomedicine Update , 5 , 100146. https://doi.org/10.1016/j.cmpbup.2024.10 0146

Kim, S. J., Lee, J. H., Park, J. H., & Hwang, H. G. (2020). Automated detection and classification of bone fractures in plain radiographs using deep convolutional neural networks. European Radiology , 30 (3), 1642–1650. https://doi.org/10.1007/s00330-019-06480 -y Le, T. H. Y., Phan, A. C., Cao, H. P., & Phan, T. C. (2020). Automatic identification of intracranial hemorrhage on CT/MRI image using meta-architectures improved from region-based CNN. In H. Le Thi, H. Le, & T. Pham Dinh (Eds.), Optimization of complex systems: Theory, models, algorithms and applications (pp. 717–726). Springer. https://doi.org/10.1007/978-3-030-21803 4_74 LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature , 521 (7553), 436–444. https://doi.org/10.1038/nature14539 Lin, H. M., Colak, E., Richards, T., Kitamura, F. C., Prevedello, L. M., Talbott, J., Ball, R. L., Gumeler, E., Yeom, K. W., Hamghalam, M., Simpson, A. L., Strika, J., Bulja, D., Angkurawaranon, S., Pérez-Lara, A., Gómez-Alonso, M. I., Ortiz Jiménez, J., Peoples, J. J., Law, M., … De Leão, R. J. (2023). The RSNA cervical spine fracture CT dataset. Radiology: Artificial Intelligence , 5 (5), e230034. https://doi.org/10.1148/ryai.230034 Lin, T.-Y., Goyal, P., Girshick, R., He, K., & Dollár, P. (2017). Focal loss for dense object detection. Proceedings of the IEEE International Conference on Computer Vision (ICCV) , 2980–2988. https://doi.org/10.1109/ICCV.2017.324

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