Abstract
Since the beginning of the COVID-19 pandemic, more than 350 million cases and 5 million deaths have occurred. Since day one, multiple methods have been provided to diagnose patients who have been infected. Alongside the gold standard of laboratory analyses, deep learning algorithms on chest X-rays (CXR) have been developed to support the COVID-19 diagnosis. The literature reports that convolutional neural networks (CNNs) have obtained excellent results on image datasets when the tests are performed in cross-validation, but such models fail to generalize to unseen data. To overcome this limitation, we exploit the strength of multiple CNNs by building an ensemble of classifiers via an optimized late fusion approach. To demonstrate the system’s robustness, we present different experiments on open source CXR datasets to simulate a real-world scenario, where scans of patients affected by various lung pathologies and coming from external datasets are tested. Promising performances are obtained both in cross-validation and in external validation, obtaining an average accuracy of 93.02% and 91.02%, respectively.
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Acknowledgements
This work is partially funded by: POR CAMPANIA FESR 2014–2020, AP1-OS1.3 project “Protocolli TC del torace a bassissima dose e tecniche di intelligenza artificiale per la diagnosi precoce e quantificazione della malattia da COVID-19” CUP D54I20001410002; EU project “University-Industrial Educational Centre in Advanced Biomedical and Medical Informatics (CeBMI) No. 612462-EPP-1-2019-1-SK-EPPKA2-KA”; “AI against COVID-19 Competition”, organized by IEEE SIGHT Montreal, Vision and Image Processing Research Group of the University of Waterloo, and DarwinAI Corp., and sponsored by Microsoft.
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Guarrasi, V., Soda, P. (2022). Optimized Fusion of CNNs to Diagnose Pulmonary Diseases on Chest X-Rays. In: Sclaroff, S., Distante, C., Leo, M., Farinella, G.M., Tombari, F. (eds) Image Analysis and Processing – ICIAP 2022. ICIAP 2022. Lecture Notes in Computer Science, vol 13231. Springer, Cham. https://doi.org/10.1007/978-3-031-06427-2_17
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DOI: https://doi.org/10.1007/978-3-031-06427-2_17
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