Representaciones basadas en aprendizaje profundo para dar soporte al diagnóstico del COVID-19 en cortes de TC

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 is dependent on the radiologist expertise, which might 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 dataset (Set-2). First, the deep representations take advantage of supervised learning models, previously trained on the natural image domain, which are adjusted following a transfer learning scheme. The deep classification was carried out: (a) via 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 5 could potentially support the COVID-19 diagnosis on clinical settings.