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Accurate detection of fresh and old vertebral compression fractures on CT images using ensemble YOLOR

  • 1237: Advanced Deep Learning for Computer Vision and Multimedia Applications
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Abstract

Vertebral compression fractures resulting from osteoporosis contribute to pain and disability among older people, necessitating early detection and treatment. While MRI provides effective diagnosis, its higher cost poses a challenge. Conversely, CT imaging offers a more cost-effective alternative. However, CT-based detection of vertebral fractures may lack the accuracy of MRI. This paper presents a novel YOLO-based object detection method for localizing old and fresh fractures on spine CT images to expedite diagnosis and identify the optimal treatment window. We propose replacing the CSPDarknet53 backbone in the native YOLOR model with MobileViT and EfficientNet_NS, training three separate YOLOR models with different backbones. Subsequently, an ensemble approach is employed to leverage the enhanced feature extraction capabilities of the three models. Experimental results show individual model accuracies of 89%, 89.8%, and 89.2%, respectively. Furthermore, by replacing the convolution layer with the Involution layer and integrating the models using the ensemble method, we achieve a remarkable accuracy rate of 93.4%. The proposed Ensemble YOLOR model surpasses other state-of-the-art approaches, offering a fast and precise solution. This advancement provides valuable insights and guidance for physicians in diagnosing compression between old and new fractures.

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Data availability

The data that support the findings of this study are not openly available due to IRB restrictions. Data are located in controlled access data storage at Dalin Tzu Chi Hospital.

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Acknowledgements

This work was partly supported by the Ministry of Science and Technology of Taiwan under grant 112-2221-E-224-033-MY3.

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Authors and Affiliations

  1. Department of Orthopedics, Dalin Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, Chiayi, Taiwan

    Min-Hong Hsieh

  2. School of Medicine, Tzu Chi University, Hualien, Taiwan

    Min-Hong Hsieh

  3. Department of Computer Science and Information Engineering, National Yunlin University of Science and Technology, Yunlin, Taiwan

    Chuan-Yu Chang & Shao-Min Hsu

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  1. Min-Hong Hsieh
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Correspondence to Chuan-Yu Chang.

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Hsieh, MH., Chang, CY. & Hsu, SM. Accurate detection of fresh and old vertebral compression fractures on CT images using ensemble YOLOR. Multimed Tools Appl 83, 89375–89391 (2024). https://doi.org/10.1007/s11042-024-20355-z

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  • DOI: https://doi.org/10.1007/s11042-024-20355-z

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