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Authors: Rohit Lokwani ; Ashrika Gaikwad ; Viraj Kulkarni ; Anirudha Pant and Amit Kharat

Affiliation: DeepTek Inc., Pune, India

Keyword(s): Artificial Intelligence, Machine Learning, Neural Networks, Deep Learning, Medical Imaging Analysis, COVID-19, Radiology.

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.

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Paper citation in several formats:
Lokwani, R.; Gaikwad, A.; Kulkarni, V.; Pant, A. and Kharat, A. (2021). Automated Detection of COVID-19 from CT Scans using Convolutional Neural Networks. In Proceedings of the 10th International Conference on Pattern Recognition Applications and Methods - ICPRAM; ISBN 978-989-758-486-2; ISSN 2184-4313, SciTePress, pages 565-570. DOI: 10.5220/0010293605650570

@conference{icpram21,
author={Rohit Lokwani. and Ashrika Gaikwad. and Viraj Kulkarni. and Anirudha Pant. and Amit Kharat.},
title={Automated Detection of COVID-19 from CT Scans using Convolutional Neural Networks},
booktitle={Proceedings of the 10th International Conference on Pattern Recognition Applications and Methods - ICPRAM},
year={2021},
pages={565-570},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010293605650570},
isbn={978-989-758-486-2},
issn={2184-4313},
}

TY - CONF

JO - Proceedings of the 10th International Conference on Pattern Recognition Applications and Methods - ICPRAM
TI - Automated Detection of COVID-19 from CT Scans using Convolutional Neural Networks
SN - 978-989-758-486-2
IS - 2184-4313
AU - Lokwani, R.
AU - Gaikwad, A.
AU - Kulkarni, V.
AU - Pant, A.
AU - Kharat, A.
PY - 2021
SP - 565
EP - 570
DO - 10.5220/0010293605650570
PB - SciTePress