A Proposal for the Deep Unsupervised Identification of Relevant Areas in X-Rays for Covid Detection
A Proposal for the Deep Unsupervised Identification of Relevant Areas in X-Rays for Covid Detection
Authors
Martinez, Jose
Nicolis, Orietta
Caro, Luis
Peralta, Billy
IEEE
Nicolis, Orietta
Caro, Luis
Peralta, Billy
IEEE
Profesor GuĆa
Authors
Date
Datos de publicaciĆ³n:
10.1109/CHILECON54041.2021.9703054
2021 IEEE CHILEAN CONFERENCE ON ELECTRICAL, ELECTRONICS ENGINEERING, INFORMATION AND COMMUNICATION TECHNOLOGIES (IEEE CHILECON 2021),Vol.,359-364,2021
2021 IEEE CHILEAN CONFERENCE ON ELECTRICAL, ELECTRONICS ENGINEERING, INFORMATION AND COMMUNICATION TECHNOLOGIES (IEEE CHILECON 2021),Vol.,359-364,2021
Tipo de recurso
Meeting
Keywords
Materia geogrƔfica
Collections
Abstract
Since the beginning of 2020, the diagnosis of the COVID-19 virus has been a major problem that has affected the lives of millions of people around the world. The detection time for COVID-19 with a standard detection method ranges from approximately 1 to 5 days. An efficient and fast way to detect the presence of both the COVID-19 virus is through the use of artificial intelligence (AI) techniques applied to images obtained by lung radiography. Typically, AI algorithms to detect COVID-19 consider the whole picture. However, there may be parts that affect the performance of the classifier. Furthermore, these algorithms do not indicate which is the most relevant area of this disease. In this work, we propose a deep learning approach to detect the presence of COVID-19 in lung images by recognizing the most relevant areas affected by the virus without considering human supervision. In the experiment, we considered different proposals, where the best one obtained an 88% reduction of the logit loss with respect to the baseline based on random regions near the center of the image.