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Biomédica
versión impresa ISSN 0120-4157versión On-line ISSN 2590-7379
Resumen
RUANO, Josué et al. Deep learning representations to support COVID-19 diagnosis on CT slices. Biomed. [online]. 2022, vol.42, n.1, pp.170-183. Epub 01-Mar-2022. ISSN 0120-4157. https://doi.org/10.7705/biomedica.5927.
Introduction:
The coronavirus disease 2019 (COVID-19) has become a significant public health problem worldwide. In this context, CT-scan automatic analysis has emerged as a COVID-19 complementary diagnosis tool allowing for radiological finding characterization, patient categorization, and disease follow-up. However, this analysis depends on the radiologist’s expertise, which may result in subjective evaluations.
Objective:
To explore deep learning representations, trained from thoracic CT-slices, to automatically distinguish COVID-19 disease from control samples.
Materials and methods:
Two datasets were used: SARS-CoV-2 CT Scan (Set-1) and FOSCAL clinic’s dataset (Set-2). The deep representations took advantage of supervised learning models previously trained on the natural image domain, which were adjusted following a transfer learning scheme. The deep classification was carried out: (a) via an end-to-end deep learning approach and (b) via random forest and support vector machine classifiers by feeding the deep representation embedding vectors into these classifiers.
Results:
The end-to-end classification achieved an average accuracy of 92.33% (89.70% precision) for Set-1 and 96.99% (96.62% precision) for Set-2. The deep feature embedding with a support vector machine achieved an average accuracy of 91.40% (95.77% precision) and 96.00% (94.74% precision) for Set-1 and Set-2, respectively.
Conclusion:
Deep representations have achieved outstanding performance in the identification of COVID-19 cases on CT scans demonstrating good characterization of the COVID-19 radiological patterns. These representations could potentially support the COVID-19 diagnosis in clinical settings.
Palabras clave : Coronavirus infections/diagnosis; tomography, X-ray computed; deep learning.