@article {10.3844/jcssp.2020.620.625,
article_type = {journal},
title = {GoogleNet CNN Neural Network towards Chest CT-Coronavirus Medical Image Classification},
author = {Alsharman, Nesreen and Jawarneh, Ibrahim},
volume = {16},
number = {5},
year = {2020},
month = {May},
pages = {620-625},
doi = {10.3844/jcssp.2020.620.625},
url = {https://thescipub.com/abstract/jcssp.2020.620.625},
abstract = {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 = {Journal of Computer Science},
publisher = {Science Publications}
}