Year: 2020
Type: article
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.
Source: Journal of Computer Science
Authors: Nesreen Alsharman, Ibrahim Jawarneh
Language: English
Topic: COVID-19 diagnosis using AI
Subfield: Radiology, Nuclear Medicine and Imaging
Field: Medicine
Domain: Health Sciences
Open Access status: hybrid
OpenAlex ID: W3024005803