TY - GEN
T1 - Deep Learning Model to Identify COVID-19 Cases from Chest Radiographs
AU - Arellano, Matias Cam
AU - Ramos, Oscar E.
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/9
Y1 - 2020/9
N2 - 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.
AB - 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.
KW - COVID-19
KW - Convolutional Neural Networks
KW - Deep Learning
KW - Medical diagnosis
UR - https://www.scopus.com/pages/publications/85095435811
U2 - 10.1109/INTERCON50315.2020.9220237
DO - 10.1109/INTERCON50315.2020.9220237
M3 - Conference contribution
AN - SCOPUS:85095435811
T3 - Proceedings of the 2020 IEEE 27th International Conference on Electronics, Electrical Engineering and Computing, INTERCON 2020
BT - Proceedings of the 2020 IEEE 27th International Conference on Electronics, Electrical Engineering and Computing, INTERCON 2020
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 27th IEEE International Conference on Electronics, Electrical Engineering and Computing, INTERCON 2020
Y2 - 3 September 2020 through 5 September 2020
ER -