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Keywords

COVID-19 detection
Image processing
Model comparison
X-Ray
Ultrasound and CT based detection
Lung image

Categories

How to Cite

G. Meenakshi, Amija Varshini, Divyashree, Brindhavanakannan, & R.Rajasri. (2022). COVID-19 Identification on Human Lung CT Images using Machine Learning. Scientific Hub of Applied Research in Engineering & Information Technology, 2(1), 07–12. https://doi.org/10.53659/shareit/2021/18

Abstract

Identifying COVID-19 at very early stage may help in considering an appropriate medication plan and contamination control decisions. In this paper, performance of COVID-19 regions using pictures from lung CT is analyzed by using ML models. PC upheld disclosure (CADe) of Coronavirus is fundamental to aiding radiologists in early distinctive verification from handled tomography (CT) channels. The momentum COVID-19 pandemic has influenced the world with over 18.35 million illnesses and more than 6, 96,147 passings up until this point (fifth August 2020). Early ID, separation, and care for patients is a basic method for better organization of this pandemic. In this work, an applied machine learning framework is used to help the detection of COVID-19 area with the usage of picture planning. The abnormal pictures are presented to division to focus on the affected gap. The request is done on features removed from the photos. The gainful methodology to perceive the Coronavirus plans to have accurate results by using KNN and Random Forest classifier using Image Processing techniques.

https://doi.org/10.53659/shareit/2021/18
  
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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Copyright (c) 2021 Scientific Hub of Applied Research in Engineering & Information Technology

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