Authors:
Walid Hariri
and
Imed Haouli
Affiliation:
Labged Laboratory, Computer Science Department, Badji Mokhtar Annaba University, Annaba, Algeria
Keyword(s):
COVID-19, CNN, Transfer Learning, Ensemble Model, X-ray Images.
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 imag
es 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.
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