Abstract
Developing robust and performant methods for diagnosing COVID-19, particularly for triaging processes, is crucial. This study introduces a completely automated system to detect COVID-19 by means of the analysis of Chest X-Ray scans (CXR). The proposed methodology is based on few-shot techniques, enabling to work on small image datasets. Moreover, a set of additions have been done to enhance the diagnostic capabilities. First, a network to extract the lung region to rely only on the most relevant image area. Second, a new cost function to penalize each misclassification according to the clinical consequences. Third, a system to combine different predictions from the same image to increase the robustness of the diagnoses. The proposed approach was validated on the public dataset COVIDGR-1.0, yielding a classification accuracy of 79.10% ± 3.41% and, thus, outperforming other state-of-the-art methods. In conclusion, the proposed methodology has proven to be suitable for the diagnosis of COVID-19.
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Acknowledgements
This work has received financial support from the Spanish Ministry of Science and Innovation under grant PID2020-112623GB-I00, Consellería de Cultura, Educación e Ordenación Universitaria under grants ED431C 2021/48, ED431G-2019/04, ED481A-2018 and ED431C 2018/29 and the European Regional Development Fund (ERDF), which acknowledges the CiTIUS-Research Center on Intelligent Technologies of the University of Santiago de Compostela as a Research Center of the Galician University System.
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Cores, D., Vila-Blanco, N., Mucientes, M., Carreira, M.J. (2023). Few-Shot Image Classification for Automatic COVID-19 Diagnosis. In: Pertusa, A., Gallego, A.J., Sánchez, J.A., Domingues, I. (eds) Pattern Recognition and Image Analysis. IbPRIA 2023. Lecture Notes in Computer Science, vol 14062. Springer, Cham. https://doi.org/10.1007/978-3-031-36616-1_43
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