Streamlining the Bone Fracture Detection Using X-Ray Imaging and Seamless PACS Data Exchange
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-61475-0_9
Reference21 articles.
1. Swapna, N., Malge, R.: Classification and detection of bone fracture using machine learning. Int. J. Res. Appl. Sci. Eng. Technol. (IJRASET) (2022). IJRASET45523. https://doi.org/10.22214/ijraset.2022.45523
2. Thian, Y.L., Li, Y., Jagmohan, P., Sia, D., Chan, V.E.Y., Tan, R.T.: Convolutional neural networks for automated fracture detection and localization on wrist radiographs. Radiol. Artif. Intell. 1, e180001 (2019). https://doi.org/10.1148/ryai.2019180001-PMC-PubMed
3. Yadav, D.P., Rathor, S.: Bone fracture detection and classification using deep learning approach. In: 2020 International Conference on Power Electronics & IoT Applications in Renewable Energy and its Control (PARC), Mathura, India, pp. 282–285 (2020). https://doi.org/10.1109/PARC49193.2020.236611
4. Yamamoto, N.: An automated fracture detection from pelvic CT images with 3-D convolutional neural networks. In: International Symposium on Community-centric Systems (CcS), Tokyo, Japan, pp. 1–6 (2020). https://doi.org/10.1109/CcS49175.2020.9231453
5. Karimunnisa, S., Savarapu, P.R., Madupu, R.K., Basha, C.Z., Neelakanteswara, P.: Detection of bone fractures automatically with enhanced performance with better combination of filtering and neural networks. In: 2020 Second International Conference on Inventive Research in Computing Applications (ICIRCA), Coimbatore, India, pp. 189–193 (2020). https://doi.org/10.1109/ICIRCA48905.2020.9183085
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