Research Article Open Access

GoogleNet CNN Neural Network towards Chest CT-Coronavirus Medical Image Classification

Nesreen Alsharman1 and Ibrahim Jawarneh2
  • 1 WISE, Jordan
  • 2 Al-Hussein Bin Talal University, Jordan


In the end of the year 2019 and the beginning of the year 2020, the world was overwhelmed by a medical pandemic that was not previously seen which is known Covid-19 (Coronavirus). Coronavirus (CoV) is a large family of viruses that cause illness ranging from the common cold to more severe diseases such as Middle East Respiratory Syndrome (MERS-CoV) and Severe Acute Respiratory Syndrome (SARS-CoV). This paper aims to improve the accuracy of detection for CT-Coronavirus images using deep learning for Convolutional Neural Networks (CNNs) that helps medical staffs for classification chest CT- Coronavirus medical image in early stage. Deep learning is successfully used as a tool for machine learning, where the CNNs are capable of automatically extracting and learning features medical image dataset. This research retrains GoogleNet CNN architecture over the COVIDCT-Dataset for classification CT- Coronavirus image. In this research, COVIDCT-Dataset contains 349 CT images containing clinical findings of COVID-19. The validation accuracy of retraining GoogleNet is 82.14% where elapsed time is 74 min and 37 sec.

Journal of Computer Science
Volume 16 No. 5, 2020, 620-625


Submitted On: 11 April 2020 Published On: 14 May 2020

How to Cite: Alsharman, N. & Jawarneh, I. (2020). GoogleNet CNN Neural Network towards Chest CT-Coronavirus Medical Image Classification. Journal of Computer Science, 16(5), 620-625.

  • 46 Citations



  • GoogleNet
  • CNN
  • CT-Coronavirus Medical Image
  • Image Classification