https://www.socictopen.socict.org/files/original/d992194f79f5b8d141b247fd1f9b0252.pdf 4d1216153a9dc423bc2d1f07fcf1001f Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Coronavirus Description An account of the resource Dominio científico: Coronavirus Text A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text. Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource COVID-19 Detection in Chest X-ray Images using a Deep Learning Approach Creator An entity primarily responsible for making the resource Iñigo Barandiaran, Fátima Saiz Description An account of the resource The Corona Virus Disease (COVID-19) is an infectious disease caused by a new virus that has not been detected in humans before. The virus causes a respiratory illness like the flu with various symptoms such as cough or fever that, in severe cases, may cause pneumonia. The COVID-19 spreads so quickly between people, affecting to 1,200,000 people worldwide at the time of writing this paper (April 2020). Due to the number of contagious and deaths are continually growing day by day, the aim of this study is to develop a quick method to detect COVID-19 in chest X-ray images using deep learning techniques. For this purpose, an object detection architecture is proposed, trained and tested with a public available dataset composed with 1500 images of non-infected patients and infected with COVID-19 and pneumonia. The main goal of our method is to classify the patient status either negative or positive COVID-19 case. In our experiments using SDD300 model we achieve a 94.92% of sensibility and 92.00% of specificity in COVID-19 detection, demonstrating the usefulness application of deep learning models to classify COVID-19 in X-ray images. Date A point or period of time associated with an event in the lifecycle of the resource 2020 Subject The topic of the resource deep learning, object detection, X-ray, coronavirus covid-19 Identifier An unambiguous reference to the resource within a given context DOI: 10.9781/ijimai.2020.04.003 Source A related resource from which the described resource is derived International Journal of Interactive Multimedia and Artificial Intelligence Publisher An entity responsible for making the resource available Universidad Internacional de La Rioja (UNIR) Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Technology