Artificial intelligence to evaluate diagnosed COVID-19 chest radiographs
We present a Machine Learning algorithm based on Python which can be used to aid COVID-19 diagnosis. This algorithm employs Convolutional Neural Networks (CNN) of ResNet-18 architecture from thoracic X-ray images to build a trained dataset that enables further comparisons between common pulmonary di...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Brazilian Radiation Protection Society (Sociedade Brasileira de Proteção Radiológica, SBPR)
2022-09-01
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| Series: | Brazilian Journal of Radiation Sciences |
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| Online Access: | https://bjrs.org.br/revista/index.php/REVISTA/article/view/2056 |
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| _version_ | 1849318474864656384 |
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| author | Bruno Takara Felipe Freitas Alexandre Bacelar Rochelle Lykawka Mirko Salomon Alva Sanchez |
| author_facet | Bruno Takara Felipe Freitas Alexandre Bacelar Rochelle Lykawka Mirko Salomon Alva Sanchez |
| author_sort | Bruno Takara |
| collection | DOAJ |
| description | We present a Machine Learning algorithm based on Python which can be used to aid COVID-19 diagnosis. This algorithm employs Convolutional Neural Networks (CNN) of ResNet-18 architecture from thoracic X-ray images to build a trained dataset that enables further comparisons between common pulmonary diseases and COVID-19 diagnosed patients to classify the radiological findings as being due the COVID-19 or other pathologies. We discuss the importance of setting the right parameters related to training and what they might represent in clinical procedures. We used a dataset containing 942 COVID-19 labeled radiographs from HCPA - Hospital das Clínicas de Porto Alegre and compared it to a public dataset from NIH Clinical Center containing images of pulmonary diseases. Lastly, our trained model had an accuracy of 81.76% for the imbalanced classes and an accuracy of 46.94% for the balanced classes, when compared to other pulmonary diseases such as pneumonia, edema, mass, consolidation, and fibrosis. These results disclose the difficulty of diagnosing COVID-19 from a chest radiograph as it resembles other pulmonary illnesses and makes room for further research in this matter. |
| format | Article |
| id | doaj-art-3a07564049364773a42ffc73e8f2cc46 |
| institution | Kabale University |
| issn | 2319-0612 |
| language | English |
| publishDate | 2022-09-01 |
| publisher | Brazilian Radiation Protection Society (Sociedade Brasileira de Proteção Radiológica, SBPR) |
| record_format | Article |
| series | Brazilian Journal of Radiation Sciences |
| spelling | doaj-art-3a07564049364773a42ffc73e8f2cc462025-08-20T03:50:49ZengBrazilian Radiation Protection Society (Sociedade Brasileira de Proteção Radiológica, SBPR)Brazilian Journal of Radiation Sciences2319-06122022-09-0110310.15392/bjrs.v10i3.20561674Artificial intelligence to evaluate diagnosed COVID-19 chest radiographsBruno Takara0Felipe FreitasAlexandre BacelarRochelle LykawkaMirko Salomon Alva Sanchez1Universidade Federal de Ciências da Saúde de Porto Alegre, Porto Alegre, Rio Grande do Sul, BrazilUFCSPAWe present a Machine Learning algorithm based on Python which can be used to aid COVID-19 diagnosis. This algorithm employs Convolutional Neural Networks (CNN) of ResNet-18 architecture from thoracic X-ray images to build a trained dataset that enables further comparisons between common pulmonary diseases and COVID-19 diagnosed patients to classify the radiological findings as being due the COVID-19 or other pathologies. We discuss the importance of setting the right parameters related to training and what they might represent in clinical procedures. We used a dataset containing 942 COVID-19 labeled radiographs from HCPA - Hospital das Clínicas de Porto Alegre and compared it to a public dataset from NIH Clinical Center containing images of pulmonary diseases. Lastly, our trained model had an accuracy of 81.76% for the imbalanced classes and an accuracy of 46.94% for the balanced classes, when compared to other pulmonary diseases such as pneumonia, edema, mass, consolidation, and fibrosis. These results disclose the difficulty of diagnosing COVID-19 from a chest radiograph as it resembles other pulmonary illnesses and makes room for further research in this matter.https://bjrs.org.br/revista/index.php/REVISTA/article/view/2056x-rayartificial inteligenceradiography |
| spellingShingle | Bruno Takara Felipe Freitas Alexandre Bacelar Rochelle Lykawka Mirko Salomon Alva Sanchez Artificial intelligence to evaluate diagnosed COVID-19 chest radiographs Brazilian Journal of Radiation Sciences x-ray artificial inteligence radiography |
| title | Artificial intelligence to evaluate diagnosed COVID-19 chest radiographs |
| title_full | Artificial intelligence to evaluate diagnosed COVID-19 chest radiographs |
| title_fullStr | Artificial intelligence to evaluate diagnosed COVID-19 chest radiographs |
| title_full_unstemmed | Artificial intelligence to evaluate diagnosed COVID-19 chest radiographs |
| title_short | Artificial intelligence to evaluate diagnosed COVID-19 chest radiographs |
| title_sort | artificial intelligence to evaluate diagnosed covid 19 chest radiographs |
| topic | x-ray artificial inteligence radiography |
| url | https://bjrs.org.br/revista/index.php/REVISTA/article/view/2056 |
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