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http://conacyt.repositorioinstitucional.mx/jspui/handle/1000/4168| Can AI help in screening Viral and COVID-19 pneumonia? | |
| Muhammad E H Chowdhury. Tawsifur Rahman. Amith Khandakar. Rashid Mazhar. Muhammad Abdul Kadir. Zaid Bin Mahbub. Khandakar R Islam. Muhammad Salman Khan. Atif Iqbal. Nasser Al-Emadi. Mamun Bin Ibne Reaz. | |
| Acceso Abierto | |
| Atribución-NoComercial-SinDerivadas | |
| https://arxiv.org/pdf/2003.13145v1.pdf | |
| Coronavirus disease (COVID-19) is a pandemic disease, which has already infected more than half a million people and caused fatalities of above 30 thousand. The aim of this paper is to automatically detect COVID-19 pneumonia patients using digital x-ray images while maximizing the accuracy in detection using image pre-processing and deep-learning techniques. A public database was created by the authors using three public databases and also by collecting images from recently published articles. The database contains a mixture of 190 COVID-19, 1345 viral pneumonia, and 1341 normal chest x-ray images. An image augmented training set was created with 2500 images of each category for training and validating four different pre-trained deep Convolutional Neural Networks (CNNs). These networks were tested for the classification of two different schemes (normal and COVID-19 pneumonia; normal, viral and COVID-19 pneumonia). The classification accuracy, sensitivity, specificity and precision for both the schemes were 98.3%, 96.7%, 100%, 100% and 98.3%, 96.7%, 99%, 100%, respectively. The high accuracy of this computer-aided diagnostic tool can significantly improve the speed and accuracy of diagnosing cases with COVID-19. This would be highly useful in this pandemic where disease burden and need for preventive measures are at odds with available resources. | |
| arxiv.org | |
| 2020 | |
| Artículo | |
| https://arxiv.org/pdf/2003.13145v1.pdf | |
| Inglés | |
| VIRUS RESPIRATORIOS | |
| Aparece en las colecciones: | Artículos científicos |
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