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 |
| Published: |
Brazilian Radiation Protection Society (Sociedade Brasileira de Proteção Radiológica, SBPR)
2022-09-01
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| Series: | Brazilian Journal of Radiation Sciences |
| Subjects: | |
| Online Access: | https://bjrs.org.br/revista/index.php/REVISTA/article/view/2056 |
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| Summary: | 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. |
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| ISSN: | 2319-0612 |