Diagnosis of COVID-19 Using Artificial Intelligence Techniques
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Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-19-4162-7_19
Reference23 articles.
1. Carvalho, E. D., Carvalho, E. D., de Carvalho Filho, A. O., de Araújo, F. H. D., & Andrade Lira Rabêlo, R. D. (2020). Diagnosis of COVID19 in CT imageusing CNN and XGBoost. In IEEE Symposium on Computers and Communications (ISCC), Rennes, France (pp. 1–6).https://doi.org/10.1109/ISCC50000.2020.9219726
2. Sekeroglu, B., & Ozsahin, I. (2020). Detection of COVID19 from chest X-ray images using convolutional neural networks. SLAS TECHNOLOGY: Translating Life Sciences Innovation. https://doi.org/10.1177/2472630320958376
3. Shuja, J., Alanazi, E., Alasmary, W., et al. (2020). COVID19 open source data sets: A comprehensive survey. Applied Intelligence. https://doi.org/10.1007/s10489-020-01862-6
4. Carvalho, E. D., de Carvalho Filho, A. O., de Sousa, A. D., Silva, A. C., & Gattass, M. (2018). Method of differentiation of benign and malignant masses in digital mammograms using texture analysis based on phylogenetic diversity. Computers Electrical Engineering, 67, 210–222.
5. de Carvalho, A. S. V., Jr., Carvalho, E. D., de Carvalho Filho, A. O., de Sousa, A. D., Silva, A. C., & Gattass, M. (2018). Automatic methods for diagnosis of glaucoma using texture descriptors based on phylogenetic diversity. Computers Electrical Engineering, 71, 102–114.
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