Welcome to the InfoSci Platform
Reference Hub1
Coronavirus Pneumonia Classification Using X-Ray and CT Scan Images With Deep Convolutional Neural Network Models

Coronavirus Pneumonia Classification Using X-Ray and CT Scan Images With Deep Convolutional Neural Network Models

Brahami Menaouer, Dermane Zoulikha, Kebir Nour El-Houda, Sabri Mohammed, Nada Matta
Copyright: © 2022 |Volume: 15 |Issue: 1 |Pages: 23
ISSN: 1938-7857|EISSN: 1938-7865|EISBN13: 9781683180340|DOI: 10.4018/JITR.299391
Cite Article Cite Article

MLA

Menaouer, Brahami, et al. "Coronavirus Pneumonia Classification Using X-Ray and CT Scan Images With Deep Convolutional Neural Network Models." JITR vol.15, no.1 2022: pp.1-23. http://doi.org/10.4018/JITR.299391

APA

Menaouer, B., Zoulikha, D., El-Houda, K. N., Mohammed, S., & Matta, N. (2022). Coronavirus Pneumonia Classification Using X-Ray and CT Scan Images With Deep Convolutional Neural Network Models. Journal of Information Technology Research (JITR), 15(1), 1-23. http://doi.org/10.4018/JITR.299391

Chicago

Menaouer, Brahami, et al. "Coronavirus Pneumonia Classification Using X-Ray and CT Scan Images With Deep Convolutional Neural Network Models," Journal of Information Technology Research (JITR) 15, no.1: 1-23. http://doi.org/10.4018/JITR.299391

Export Reference

Mendeley
Favorite Full-Issue Download

Abstract

Pneumonia is a life-threatening infectious disease affecting one or both lungs in humans. There are mainly two types of pneumonia: bacterial and viral. Likewise, patients with coronavirus can develop symptoms that belong to the common flu, pneumonia, and other respiratory diseases. Chest X-rays are the common method used to diagnose coronavirus pneumonia and it needs a medical expert to evaluate the result of X-ray. Furthermore, DL has garnered great attention among researchers in recent years in a variety of application domains such as medical image processing, computer vision, bioinformatics, and many others. In this paper, we present a comparison of Deep Convolutional Neural Networks models for automatically binary classification query chest X-ray & CT images dataset with the goal of taking precision tools to health professionals based on fined recent versions of ResNet50, InceptionV3, and VGGNet. The experiments were conducted using a chest X-ray & CT open dataset of 5856 images and confusion matrices are used to evaluate model performances.