Name
Abstract | ||
|---|---|---|
Detecting and identifying patterns in chest X-ray images of Covid-19 patients are important tasks for understanding the disease and for making differential diagnosis. Given the relatively small number of available Covid-19 X-ray images and the need to make progress in understanding the disease, we propose a transfer learning technique applied to a pretrained VGG19 neural network to build a deep convolutional model capable of detecting four possible conditions: normal (healthy), bacteria, virus (not Covid-19), and Covid-19. The transformation of the multi-class deep learning output into binary outputs and the detection of Covid-19 image patterns using Grad-CAM technique show promising results. The discovered patterns are consistent across images from a given class of disease and constitute explanations of how the deep learning model makes classification decisions. In the long run, the identified patterns can serve as biomarkers for a given disease in chest X-ray images. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/BIBE50027.2020.00077 | 2020 IEEE 20th International Conference on Bioinformatics and Bioengineering (BIBE) |
Keywords | DocType | ISSN |
Neural Networks,Biomarkers,Covid-19,Artificial Intelligence | Conference | 2159-5410 |
ISBN | Citations | PageRank |
978-1-7281-9575-9 | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mirtha Lucas | 1 | 0 | 0.68 |
| Miguel Lerma | 2 | 0 | 0.34 |
| Jacob D. Furst | 3 | 545 | 56.63 |
| Daniela Stan Raicu | 4 | 469 | 46.22 |