Abstract
The Computer tomography Scan imaging technique becomes an alternative approach of RTPCR test in COVID-19 diagnosis, disease staging and monitoring of treatment response evolution. The sensitivity of RTPCT is low neat about 70% with compare to the sensitivity of chest CT Scan images technique up to 98%. In this work, COVID-19 CT Scan image segmentation has been performed. The UNET Model has been selected as the baseline model for CT Scan Image Segmentation. The CT Scan Image segmentation performance of the deep learning models has been improved using the transfer learning approach. The IoU values have been improved from 86.94 to 88.90, 93.56, 94.34 using MobileNet as an encoder, DenseNet201 as an encoder, DenseNet169 as an encoder in Baseline UNET Models respectively.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: International Research Journal of Computer Science
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
