Author: Castro, Jose David Bermudez; Rei, Ricardo; Ruiz, Jose E.; Diaz, Pedro Achanccaray; Canchumuni, Smith Arauco; Villalobos, Cristian Munoz; Coelho, Felipe Borges; Mendoza, Leonardo Forero; Pacheco, Marco Aurelio C.
Title: A free web service for fast COVID-19 classification of chest X-Ray images Cord-id: teckn34w Document date: 2020_8_27
ID: teckn34w
Snippet: 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 i
Document: 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.
Search related documents:
Co phrase search for related documents- accuracy achieve and loss function: 1, 2
- accuracy loss and adam optimizer: 1
- accuracy loss and loss function: 1, 2, 3, 4, 5, 6, 7, 8, 9
- accuracy loss function and loss function: 1, 2, 3, 4, 5, 6
- accuracy validation set and acute respiratory syndrome: 1
- accurate model and acute respiratory syndrome: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10
- accurate model and adam optimizer: 1
- accurate model and loss function: 1, 2, 3, 4, 5, 6
- active case and acute respiratory syndrome: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25
- actual test and acute respiratory syndrome: 1, 2, 3
- acute respiratory syndrome and adam optimizer: 1
- acute respiratory syndrome and loss function: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25
- acute respiratory syndrome and low false positive: 1, 2, 3
- acute respiratory syndrome and lung finding: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16
- acute respiratory syndrome and lung finding opacity: 1
- adam optimizer and loss function: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10
- adam optimizer learning rate and loss function: 1, 2
Co phrase search for related documents, hyperlinks ordered by date