COV-VGX: An automated COVID-19 detection system using X-ray images and transfer learning

Informatics in Medicine Unlocked

O.F.M. Riaz Rahman Aranya¹  Muhammad Sheikh Sadi²  Sadia Jahan³  Prottoy Saha⁴  Ferdib-Al Islam⁵ 

[1] Khulna University of Engineering &Technology, Department of Computer Science and Engineering, Khulna, 9203, Bangladesh.;Technology, Department of Computer Science and Engineering, Khulna, Bangladesh;;Corresponding author. Khulna University of Engineering &;Khulna University of Engineering &

关键词: COVID-19;    X-ray images;    Transfer learning;    VGG-16;    Deep learning;   

DOI  :  

来源: DOAJ

【 摘 要 】

Coronavirus (COVID-19) has been one of the most dangerous and acute deadly diseases across the world recently. Researchers are trying to develop automated and feasible COVID-19 detection systems with the help of deep neural networks, machine learning techniques, etc. In this paper, a deep learning-based COVID-19 detection system called COV-VGX is proposed that contributes to detecting coronavirus disease automatically using chest X-ray images. The system introduces two types of classifiers, namely, a multiclass classifier that automatically predicts coronavirus, pneumonia, and normal classes and a binary classifier that predicts coronavirus and pneumonia classes. Using transfer learning, a deep CNN model is proposed to extract distinct and high-level features from X-ray images in collaboration with the pretrained model VGG-16. Despite the limitation of the COVID-19 dataset, the model is evaluated with sufficient COVID-19 images. Extensive experiments for multiclass classifier have achieved 98.91% accuracy, 97.31% precision, 99.50% recall, 98.39% F1-score, while 99.37% accuracy, 98.76% precision, 100% recall, 99.38% F1-score for binary classifier. The proposed system can contribute a lot in diagnosing COVID-19 effectively in the medical field.

【 授权许可】

Unknown