Medical Image Classification for Coronavirus Disease (COVID-19) Using Convolutional Neural Networks

  • Ehsan Ali Al-Zubaidi Ehsan Ali Al-Zubaidi, University of Kufa, IRAQ
  • Maad M. Mijwil Baghdad College of Economic Sciences University, IRAQ
Keywords: CT- coronavirus image, GoogleNet, COVID-19, Deep learning, Convolutional neural networks

Abstract

     The coronavirus is a family of viruses that cause different dangerous diseases that lead to death. Two types of this virus have been previously found: SARS-CoV, which causes a severe respiratory syndrome, and MERS-CoV, which causes a respiratory syndrome in the Middle East. The latest coronavirus, originated in the Chinese city of Wuhan, is known as the COVID-19 pandemic. It is a new kind of coronavirus that can harm people and was first discovered in Dec. 2019. According to the statistics of the World Health Organization (WHO), the number of people infected with this serious disease has reached more than seven million people from all over the world. In Iraq, the number of people infected has reached more than twenty-two thousand people until April 2020. In this article, we have applied convolutional neural networks (ConvNets) for the detection of the accuracy of computed tomography (CT) coronavirus images that assist medical staffs in hospitals on categorization chest CT-coronavirus images at an early stage. The ConvNets are able to automatically learn and extract features from the medical image dataset. The objective of this study is to train the GoogleNet ConvNet architecture, using the COVID-CT dataset, to classify 425 CT-coronavirus images. The experimental results show that the validation accuracy of GoogleNet in training the dataset is 82.14% with an elapsed time of 74 minutes and 37 seconds.

Published
2021-08-31
How to Cite
Al-Zubaidi, E. A., & Mijwil, M. M. (2021). Medical Image Classification for Coronavirus Disease (COVID-19) Using Convolutional Neural Networks. Iraqi Journal of Science, 62(8), 2740-2747. https://doi.org/10.24996/ijs.2021.62.8.27
Section
Computer Science