A free web service for fast COVID-19 classification of chest X-Ray images

• URL: http://arxiv.org/abs/2009.01657v1
• Date: Thu, 27 Aug 2020 20:53:26 GMT
• Title: A free web service for fast COVID-19 classification of chest X-Ray images
• Authors: Jose David Bermudez Castro, Ricardo Rei, Jose E. Ruiz, Pedro Achanccaray Diaz, Smith Arauco Canchumuni, Cristian Mu\~noz Villalobos, Felipe Borges Coelho, Leonardo Forero Mendoza, and Marco Aurelio C. Pacheco
• Abstract summary: This work provides a fast detection system of COVID-19 characteristics in X-Ray images based on deep learning (DL) techniques. The whole system can filter out non-chest X-Ray images, and detect whether the X-Ray presents characteristics of COVID-19.
• Score: 5.1263294745542405
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The coronavirus outbreak became a major concern for society worldwide. Technological innovation and ingenuity are essential to fight COVID-19 pandemic and bring us one step closer to overcome it. Researchers over the world are working actively to find available alternatives in different fields, such as the Healthcare System, pharmaceutic, health prevention, among others. With the rise of artificial intelligence (AI) in the last 10 years, IA-based applications have become the prevalent solution in different areas because of its higher capability, being now adopted to help combat against COVID-19. This work provides a fast detection system of COVID-19 characteristics in X-Ray images based on deep learning (DL) techniques. This system is available as a free web deployed service for fast patient classification, alleviating the high demand for standards method for COVID-19 diagnosis. It is constituted of two deep learning models, one to differentiate between X-Ray and non-X-Ray images based on Mobile-Net architecture, and another one to identify chest X-Ray images with characteristics of COVID-19 based on the DenseNet architecture. For real-time inference, it is provided a pair of dedicated GPUs, which reduce the computational time. The whole system can filter out non-chest X-Ray images, and detect whether the X-Ray presents characteristics of COVID-19, highlighting the most sensitive regions.

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