Deep Learning Model to Identify COVID-19 Cases from Chest Radiographs
| Autor: | Matias Cam Arellano, Oscar E. Ramos |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
medicine.medical_specialty
medicine.diagnostic_test Coronavirus disease 2019 (COVID-19) Computer science business.industry Deep learning Radiography Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) education Clinical settings Task (project management) medicine Medical physics Artificial intelligence Medical diagnosis Chest radiograph business |
| Zdroj: | 2020 IEEE XXVII International Conference on Electronics, Electrical Engineering and Computing (INTERCON) |
| DOI: | 10.1109/intercon50315.2020.9220237 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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