Author: Schlotterbeck, J. N.; Montoya, C. E.; Bitar, P.; Fuentes, J. A.; Dinamarca, V.; Rojas, G. M.; Galvez, M.
Title: Automatic analysis system of COVID-19 radiographic lung images (XrayCoviDetector) Cord-id: 38yh7sxy Document date: 2020_8_23
ID: 38yh7sxy
Snippet: COVID-19 is a pandemic infectious disease caused by the SARS-CoV-2 virus, having reached more than 210 countries and territories. It produces symptoms such as fever, dry cough, dyspnea, fatigue, pneumonia, and radiological manifestations. The most common reported RX and CT findings include lung consolidation and ground-glass opacities. In this paper, we describe a machine learning-based system (XrayCoviDetector; www.covidetector.net), that detects automatically, the probability that a thorax rad
Document: COVID-19 is a pandemic infectious disease caused by the SARS-CoV-2 virus, having reached more than 210 countries and territories. It produces symptoms such as fever, dry cough, dyspnea, fatigue, pneumonia, and radiological manifestations. The most common reported RX and CT findings include lung consolidation and ground-glass opacities. In this paper, we describe a machine learning-based system (XrayCoviDetector; www.covidetector.net), that detects automatically, the probability that a thorax radiological image includes COVID-19 lung patterns. XrayCoviDetector has an accuracy of 0.93, a sensitivity of 0.96, and a specificity of 0.90.
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