Artikel: IKONOS: an intelligent tool to support diagnosis of COVID-19 by texture analysis of X-ray images
Res. Biomed. Eng.
Abstract: Purpose: In late 2019, the SARS-CoV-2 virus spread worldwide. The virus has high rates of proliferation and causes severe respiratory symptoms, such as pneumonia. The standard diagnostic method for pneumonia is chest X-ray image. There are many ... ...
| Abstract | Purpose: In late 2019, the SARS-CoV-2 virus spread worldwide. The virus has high rates of proliferation and causes severe respiratory symptoms, such as pneumonia. The standard diagnostic method for pneumonia is chest X-ray image. There are many advantages to using COVID-19 diagnostic X-rays: low cost, fast, and widely available. Methods: We propose an intelligent system to support diagnosis by X-ray images. We tested Haralick and Zernike moments for feature extraction. Experiments with classic classifiers were done. Results: Support vector machines stood out, reaching an average accuracy of 89.78%, average sensitivity of 0.8979, and average precision and specificity of 0.8985 and 0.9963, respectively. Conclusion: Using features based on textures and shapes combined with classical classifiers, the developed system was able to differentiate COVID-19 from viral and bacterial pneumonia with low computational cost. |
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| Schlagwörter | covid19 |
| Verlag | WHO |
| Dokumenttyp | Artikel |
| Anmerkung | WHO #Covidence: #747111 |
| Datenquelle | COVID19 |
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