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In Biomedica : revista del Instituto Nacional de Salud

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.

Ruano Josué, Arcila John, Romo-Bucheli David, Vargas Carlos, Rodríguez Jefferson, Mendoza Óscar, Plazas Miguel, Bautista Lola, Villamizar Jorge, Pedraza Gabriel, Moreno Alejandra, Valenzuela Diana, Vázquez Lina, Valenzuela-Santos Carolina, Camacho Paul, Mantilla Daniel, Martínez Carrillo Fabio