Automated Detection of COVID-19 from CT Scans using Convolutional Neural Networks
Proceedings of the 10th International Conference on Pattern Recognition Applications and Methods
; : 565-570, 2021.
Article
in English
| Web of Science | ID: covidwho-1304803
ABSTRACT
COVID-19 is an infectious disease that causes respiratory problems similar to those caused by SARS-CoV (2003). In this paper, we propose a prospective screening tool wherein we use chest CT scans to diagnose the patients for COVID-19 pneumonia. We use a set of open-source images, available as individual CT slices, and full CT scans from a private Indian Hospital to train our model. We build a 2D segmentation model using the U-Net architecture, which gives the output by marking out the region of infection. Our model achieves a sensitivity of 0.96 (95% CI 0.88-1.00) and a specificity of 0.88 (95% CI 0.82-0.94). Additionally, we derive a logic for converting our slice-level predictions to scan-level, which helps us reduce the false positives.
Full text:
Available
Collection:
Databases of international organizations
Database:
Web of Science
Language:
English
Journal:
Proceedings of the 10th International Conference on Pattern Recognition Applications and Methods
Year:
2021
Document Type:
Article
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