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http://dx.doi.org/10.56977/jicce.2022.20.3.219

SVM on Top of Deep Networks for Covid-19 Detection from Chest X-ray Images  

Do, Thanh-Nghi (Department of Computer Networks, Can Tho University)
Le, Van-Thanh (Tam Anh Hospital)
Doan, Thi-Huong (Healthcare Center, National Assembly)
Publication Information
Journal of information and communication convergence engineering / v.20, no.3, 2022 , pp. 219-225 More about this Journal
Abstract
In this study, we propose training a support vector machine (SVM) model on top of deep networks for detecting Covid-19 from chest X-ray images. We started by gathering a real chest X-ray image dataset, including positive Covid-19, normal cases, and other lung diseases not caused by Covid-19. Instead of training deep networks from scratch, we fine-tuned recent pre-trained deep network models, such as DenseNet121, MobileNet v2, Inception v3, Xception, ResNet50, VGG16, and VGG19, to classify chest X-ray images into one of three classes (Covid-19, normal, and other lung). We propose training an SVM model on top of deep networks to perform a nonlinear combination of deep network outputs, improving classification over any single deep network. The empirical test results on the real chest X-ray image dataset show that deep network models, with an exception of ResNet50 with 82.44%, provide an accuracy of at least 92% on the test set. The proposed SVM on top of the deep network achieved the highest accuracy of 96.16%.
Keywords
Covid-19; X-ray image; Deep learning; Support vector machines;
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