TY  - JOUR
AU  - Alsharman, Nesreen 
AU  - Jawarneh, Ibrahim 
PY  - 2020
TI  - GoogleNet CNN Neural Network towards Chest CT-Coronavirus Medical Image Classification
JF  - Journal of Computer Science
VL  - 16
IS  - 5
DO  - 10.3844/jcssp.2020.620.625
UR  - https://thescipub.com/abstract/jcssp.2020.620.625
AB  - 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.