Advanced Deep Transfer Learning Using Ensemble Models for COVID-19 Detection from X-ray Images

Walid Hariri, Imed Haouli

2023

Abstract

The pandemic of Coronavirus disease (COVID-19) has become one of the main causes of mortality over the world. In this paper, we employ a transfer learning-based method using five pre-trained deep convolutional neural networks (CNN) architectures fine-tuned with an X-ray image dataset to detect COVID-19. Hence, we use VGG-16, ResNet50, InceptionV3, ResNet101 and Inception-ResNetV2 models in order to classify the input images into three classes (COVID-19 / Healthy / Other viral pneumonia). The results of each model are presented in detail using 10-fold cross-validation and comparative analysis has been given among these models by taking into account different elements in order to find the more suitable model. To further enhance the performance of single models, we propose to combine the obtained predictions of these models using the majority vote strategy. The proposed method has been validated on a publicly available chest X-ray image database that contains more than one thousand images per class. Evaluation measures of the classification performance have been reported and discussed in detail. Promising results have been achieved compared to state-of-the-art methods where the proposed ensemble model achieved higher performance than using any single model. This study gives more insights to researchers for choosing the best models to accurately detect the COVID-19 virus.

Paper Citation

in Harvard Style

Hariri W. and Haouli I. (2023). Advanced Deep Transfer Learning Using Ensemble Models for COVID-19 Detection from X-ray Images. In Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2023) - Volume 4: VISAPP; ISBN 978-989-758-634-7, SciTePress, pages 355-362. DOI: 10.5220/0011703900003417

in Bibtex Style

@conference{visapp23,
author={Walid Hariri and Imed Haouli},
title={Advanced Deep Transfer Learning Using Ensemble Models for COVID-19 Detection from X-ray Images},
booktitle={Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2023) - Volume 4: VISAPP},
year={2023},
pages={355-362},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011703900003417},
isbn={978-989-758-634-7},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2023) - Volume 4: VISAPP
TI - Advanced Deep Transfer Learning Using Ensemble Models for COVID-19 Detection from X-ray Images
SN - 978-989-758-634-7
AU - Hariri W.
AU - Haouli I.
PY - 2023
SP - 355
EP - 362
DO - 10.5220/0011703900003417
PB - SciTePress