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.
