Next Article in Journal
Special Issue: Eco-Novel Food and Feed
Previous Article in Journal
Developments, Trends, and Challenges in Optimization of Ship Energy Systems
Open AccessArticle

Deep Learning System for COVID-19 Diagnosis Aid Using X-ray Pulmonary Images

Architecture and Computer Technology Department (Universidad de Sevilla), E.T.S Ingeniería Informática, Reina Mercedes Avenue, 41012 Seville, Spain
*
Author to whom correspondence should be addressed.
Appl. Sci. 2020, 10(13), 4640; https://doi.org/10.3390/app10134640
Received: 2 June 2020 / Revised: 22 June 2020 / Accepted: 2 July 2020 / Published: 5 July 2020
The spread of the SARS-CoV-2 virus has made the COVID-19 disease a worldwide epidemic. The most common tests to identify COVID-19 are invasive, time consuming and limited in resources. Imaging is a non-invasive technique to identify if individuals have symptoms of disease in their lungs. However, the diagnosis by this method needs to be made by a specialist doctor, which limits the mass diagnosis of the population. Image processing tools to support diagnosis reduce the load by ruling out negative cases. Advanced artificial intelligence techniques such as Deep Learning have shown high effectiveness in identifying patterns such as those that can be found in diseased tissue. This study analyzes the effectiveness of a VGG16-based Deep Learning model for the identification of pneumonia and COVID-19 using torso radiographs. Results show a high sensitivity in the identification of COVID-19, around 100%, and with a high degree of specificity, which indicates that it can be used as a screening test. AUCs on ROC curves are greater than 0.9 for all classes considered. View Full-Text
Keywords: COVID-19; pandemic; deep learning; neural networks; X-ray; medical images COVID-19; pandemic; deep learning; neural networks; X-ray; medical images
Show Figures

Figure 1

MDPI and ACS Style

Civit-Masot, J.; Luna-Perejón, F.; Domínguez Morales, M.; Civit, A. Deep Learning System for COVID-19 Diagnosis Aid Using X-ray Pulmonary Images. Appl. Sci. 2020, 10, 4640.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop