Deep Learning Model to Identify COVID-19 Cases from Chest Radiographs

Matias Cam Arellano, Oscar E. Ramos

Producción científica: Capítulo del libro/informe/acta de congresoContribución a la conferenciarevisión exhaustiva

6 Citas (Scopus)

Resumen

The interpretation of radiographs is critical for the detection of many diseases, specially in the thoracic part, which is where COVID-19 attacks. Many people around the world are suffering from this disease, because of the easy spread of the virus. In an attempt to help physicians in their diagnosis of COVID-19, since it can be seen from a frontal view chest radiograph, deep learning approaches have recently been introduced to deal with this detection task. The purpose of this work is to investigate how well current deep learning algorithms perform on the detection of COVID-19, and to give hints on how the approach can be used in the future on real clinical settings, to help professional radiologists.

Idioma originalInglés
Título de la publicación alojadaProceedings of the 2020 IEEE 27th International Conference on Electronics, Electrical Engineering and Computing, INTERCON 2020
EditorialInstitute of Electrical and Electronics Engineers Inc.
ISBN (versión digital)9781728193779
DOI
EstadoPublicada - set. 2020
Evento27th IEEE International Conference on Electronics, Electrical Engineering and Computing, INTERCON 2020 - Virtual, Lima, Perú
Duración: 3 set. 20205 set. 2020

Serie de la publicación

NombreProceedings of the 2020 IEEE 27th International Conference on Electronics, Electrical Engineering and Computing, INTERCON 2020

Conferencia

Conferencia27th IEEE International Conference on Electronics, Electrical Engineering and Computing, INTERCON 2020
País/TerritorioPerú
CiudadVirtual, Lima
Período3/09/205/09/20

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