Author
Listed:
- Juan Eduardo Luján-García
(Centro de Investigación en Computación, Instituto Politécnico Nacional, Mexico City 07700, Mexico)
- Marco Antonio Moreno-Ibarra
(Centro de Investigación en Computación, Instituto Politécnico Nacional, Mexico City 07700, Mexico)
- Yenny Villuendas-Rey
(Centro de Innovación y Desarrollo Tecnológico en Cómputo, Instituto Politécnico Nacional, Mexico City 07700, Mexico)
- Cornelio Yáñez-Márquez
(Centro de Investigación en Computación, Instituto Politécnico Nacional, Mexico City 07700, Mexico)
Abstract
As of the end of 2019, the world suffered from a disease caused by the SARS-CoV-2 virus, which has become the pandemic COVID-19. This aggressive disease deteriorates the human respiratory system. Patients with COVID-19 can develop symptoms that belong to the common flu, pneumonia, and other respiratory diseases in the first four to ten days after they have been infected. As a result, it can cause misdiagnosis between patients with COVID-19 and typical pneumonia. Some deep-learning techniques can help physicians to obtain an effective pre-diagnosis. The content of this article consists of a deep-learning model, specifically a convolutional neural network with pre-trained weights, which allows us to use transfer learning to obtain new retrained models to classify COVID-19, pneumonia, and healthy patients. One of the main findings of this article is that the following relevant result was obtained in the dataset that we used for the experiments: all the patients infected with SARS-CoV-2 and all the patients infected with pneumonia were correctly classified. These results allow us to conclude that the proposed method in this article may be useful to help physicians decide the diagnoses related to COVID-19 and typical pneumonia.
Suggested Citation
Juan Eduardo Luján-García & Marco Antonio Moreno-Ibarra & Yenny Villuendas-Rey & Cornelio Yáñez-Márquez, 2020.
"Fast COVID-19 and Pneumonia Classification Using Chest X-ray Images,"
Mathematics, MDPI, vol. 8(9), pages 1-19, August.
Handle:
RePEc:gam:jmathe:v:8:y:2020:i:9:p:1423-:d:404000
Download full text from publisher
Corrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jmathe:v:8:y:2020:i:9:p:1423-:d:404000. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
We have no bibliographic references for this item. You can help adding them by using this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .
Please note that corrections may take a couple of weeks to filter through
the various RePEc services.