| Autor: |
Bruno Takara, Felipe Freitas, Alexandre Bacelar, Rochelle Lykawka, Mirko Salomon Alva Sanchez |
| Jazyk: |
English<br />Portuguese |
| Rok vydání: |
2022 |
| Předmět: |
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| Zdroj: |
Brazilian Journal of Radiation Sciences, Vol 10, Iss 3 (2022) |
| Druh dokumentu: |
article |
| ISSN: |
2319-0612 |
| DOI: |
10.15392/bjrs.v10i3.2056 |
| Popis: |
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. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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