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Detecting COVID-19 in X-Rays with Machine Learning

##article.authors##

  • Harshdeep Kaur
  • Guillermo Goldsztein

Keywords:

convolutional layers, covid-19, x-rays, machine learning, neural networks

Abstract

We review the main ideas and goals behind machine learning, supervised learning, neural networks, convolutional layers and apply these techniques to the problem of image classification with more than two categories (Multi-Classification). Our images are chest X-Rays and the categories are: Normal, Pneumonia, Lung Opacity, and COVID.

Author Biography

Guillermo Goldsztein

PhD , Applied Mathematics, Massachusetts Institute of Technology

Professor and Director of Undergraduate Studies, Georgia Institute of Technology 

References or Bibliography

Andriy Burkov.The hundred-page machine learning book, volume 1. Andriy Burkov Canada,2019.

Jerome Friedman, Trevor Hastie, Robert Tibshirani, et al.The elements of statistical learning,volume 1. Springer series in statistics New York, 2001.

Tom M Mitchell et al. Machine learning. 1997.

Toby Segaran.Programming collective intelligence: building smart web 2.0 applications. ”O’Reilly Media, Inc.”, 2007.

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10-13-2021

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Copyright (c) 2021 Harshdeep Kaur, Guillermo Goldsztein

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