COVIDETECT: A TKINTER DESKTOP APPLICATION TO DETECT CORONAVIRUS USING CHEST CT-SCAN

Authors

  • Sujay Pookkattuparambil Student, Department of Computer Engineering, Vasantdada Patil Pratishthan’s College Of Engineering and Visual Arts, Mumbai, Maharashtra, India
  • Janvi Patil Student, Department of Computer Engineering, Vasantdada Patil Pratishthan’s College Of Engineering and Visual Arts, Mumbai, Maharashtra, India
  • Nidhi Gosavi Student, Department of Computer Engineering, Vasantdada Patil Pratishthan’s College Of Engineering and Visual Arts, Mumbai, Maharashtra, India
  • Atul Shintre Professor, Department of Computer Engineering, Vasantdada Patil Pratishthan’s College Of Engineering and Visual Arts, Mumbai, Maharashtra, India

DOI:

https://doi.org/10.17605/OSF.IO/C4N6R

Keywords:

Neural Networks, Deep Learning, Convolutional Neural Network

Abstract

We are currently facing the threat of the Coronavirus Pandemic. The daily lives of people have come to a standstill. Tests to find out whether a person has contracted the virus are being conducted on a large scale. Due to this, the results are taking a long time to be released to the general people. This report proposes a desktop application built using the tkinter library which will help to detect COVID by checking for abnormalities in CT-Scan. This system tries to give a somewhat accurate prediction of whether the user is infected by coronavirus or not. This system basically tries to help the user to make use of his/her CT-SCAN images to predict corona virus in minimum time as compared to traditional tests. This system can be used as a primary or an intermediary step in the detection of the virus.

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Published

2021-05-19

Issue

Vol. 8 No. 05 (2021): IJIERT

Section

Articles

License

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How to Cite

Sujay Pookkattuparambil, Janvi Patil, Nidhi Gosavi, & Atul Shintre. (2021). COVIDETECT: A TKINTER DESKTOP APPLICATION TO DETECT CORONAVIRUS USING CHEST CT-SCAN. International Journal of Innovations in Engineering Research and Technology, 8(05), 142-147. https://doi.org/10.17605/OSF.IO/C4N6R