Name
Abstract | ||
|---|---|---|
In clinical diagnosis, automatic fracture detection can reduce misdiagnosis which caused by fatigue and inexperience of radiologists, and provide support for reducing patient suffering and preventing disease progression. This paper proposes a SK-DenseNet model to detect femur fracture based on DenseNet network. It uses SK module to adjust the size of receptive fields adaptively, adopts Focal loss function to update parameter, and uses Grad-CAM method to visualize the femoral detection results. It can improve the interpretability of the fracture detection model. This paper used the femoral dataset to verify the performance of the mode. Our model achieves an accuracy of 0.9117, with the kappa coefficient is 0.8228. The experimental results show that the performance of the proposed model is better than the traditional deep learning model.
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Year | DOI | Venue |
|---|---|---|
2019 | 10.1145/3358331.3358402 | Proceedings of the 2019 International Conference on Artificial Intelligence and Advanced Manufacturing |
Keywords | Field | DocType |
CAD, Deep learning, Femoral fracture, SK-DenseNet | Nuclear medicine,Femur,Medicine | Conference |
ISBN | Citations | PageRank |
978-1-4503-7202-2 | 0 | 0.34 |
References | Authors | |
0 | 9 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| yu miao | 1 | 4 | 7.18 |
| Peng-Fei Zhao | 2 | 0 | 0.34 |
| Xiong-Feng Tang | 3 | 0 | 0.34 |
| Yu-Qin Li | 4 | 0 | 0.68 |
| Li-Yuan Zhang | 5 | 0 | 0.34 |
| Wei-Li Shi | 6 | 0 | 0.34 |
| Ke Zhang | 7 | 0 | 0.34 |
| Huamin Yang | 8 | 19 | 17.29 |
| Jian-Hua Liu | 9 | 0 | 0.34 |