Naïve method of identification of COVID-19 Infection using X-Ray Image

Authors

  • Anupam Kumar Singh  Research Scholar, SHEAT COLLEGE OF ENGINEERING, VARANASI, India
  • Shailesh Kumar Singh  SHEAT COLLEGE OF ENGINEERING, VARANASI, India

Keywords:

COVID-19 Infection, X-Ray Image, COVIDX-Net, MobileNetV2, Xception, InceptionV3, ResNetV2, DenseNet121, VGG19

Abstract

It is anticipated that severe pneumonia due to the COVID-19 would significantly impact medical services. Accurate diagnosis is crucial to reducing stress on the healthcare system. Imaging techniques such as chest X-rays and CT scans are often used to diagnose pneumonia. Despite CT scan being the gold standard, CXRs are still useful because they are more widespread, faster, and cheaper. This study aims to determine whether or not CXR images alone are sufficient for differentiating COVID-19 pneumonia from other types of pneumonia and healthy lungs. COVID-19, short for Novel Coronavirus Disease, is the name given to the virus that was identified late in 2019 in China and is considered exceedingly infectious. SARS-CoV-2 is a coronavirus, like many others, that may cause serious sickness. The sickness emerged in Wuhan, China, in December of 2019 and has since spread to more than 213 nations. People infected with COVID-19 often have a high body temperature, dry cough, and acute weariness. A multiclass and a hierarchical pneumonia classification were considered in developing our classification method. Because of the uneven data distribution in this region, we also suggested including resampling methods into the schema to re-balance the classes. Our results indicate that texture is one of the key visual elements of CXR photos, and our classification schema extracts features using a pre-trained CNN model and a set of well-known texture descriptors. We also explored early and late fusion techniques inside the schema to take advantage of the capabilities of many texture descriptors and base classifiers concurrently.

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International Journal of Scientific Research in Science and Technology

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Published

2022-02-05

Issue

Volume 9, Issue 1, January-February-2022

Section

Research Articles

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How to Cite

[1]
Anupam Kumar Singh, Shailesh Kumar Singh "Naïve method of identification of COVID-19 Infection using X-Ray Image" International Journal of Scientific Research in Science and Technology(IJSRST), Online ISSN : 2395-602X, Print ISSN : 2395-6011,Volume 9, Issue 1, pp.464-474, January-February-2022.