X-Ray Images Analysis by Medium Artificial Neural Network

  • Darwin Patiño Perez Universidad de Guayaquil
  • Ricardo Silva Bustillos Villanova University https://orcid.org/0000-0001-9390-3388
  • Miguel Botto-Tobar Universidad de Guayaquil
  • Celia Munive Mora DeSales University
Keywords: Covid-19, X-ray, machine learning, prediction model, convolutional neural networks

Abstract

Currently the world is affected by a new strain of coronavirus called SARS-2, which is the cause of a respiratory-type infectious disease called Covid-19; the symptoms are fever, dry cough, shortness of breath, tiredness and in some more severe cases it can cause pneumonia, leading to death. According to the world health organization, the disease originated in Wuhan-China and spread rapidly throughout the world, causing serious health problems for populations without finding an effective cure or treatment to help prevent death and control its spread. Health specialists have not been able to find an effective cure that prevents the spread of the virus, although there are mechanisms to detect the disease, one of the most effective is related to the analysis of X-ray images of the chest of a patient; Manually processing a group of patient images is time consuming, so processing large volumes of images makes it impossible to promptly treat patients if the virus is detected. In the present manuscript, an X-ray image analysis mechanism is exposed, which uses artificial intelligence; and through a machine learning technique, through an algorithm based on artificial neural networks, a program is able to apply machine learning and learn to recognize patterns in chest images of infected and healthy patients, so that it can classify, predict and detect one if a new image is of a infected or healthy patient.

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Published
2021-03-31
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Abstract 80
PDF (Español (España)) 60
How to Cite
Patiño Perez, D., Silva Bustillos, R., Botto-Tobar, M., & Munive Mora, C. (2021). X-Ray Images Analysis by Medium Artificial Neural Network. Ecuadorian Science Journal, 5(1), 55-60. https://doi.org/10.46480/esj.5.1.50
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Review Paper
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