Efficient COVID-19 Prediction by Merging Various Deep Learning Architectures - Oraibi, Zakariya A.; Albasri, Safaa (Informatica, 2024)
In late 2019, COVID-19 virus emerged as a dangerous disease that led to millions of fatalities and changed how human beings interact with each other and forced people to wear masks with mandatory lockdown. The ability to diagnose and detect this novel disease can help in isolating the infected patients and curb the spread of the virus. Artificial intelligence techniques including machine learning showed huge potential in accurately classifying COVID-19 chest X-ray images. In this paper, we propose to combine multiple powerful CNN models (Xception, VGG-16, VGG-19) using the rule of sum. Each of these models is trained from scratch and tested on the given test images. The dataset was collected from a large public repository of COVID images with three classes: COVID, Normal, and Pneumonia. During experiments, data augmentation is also applied to provide more training samples. Experimental results show that combining multiple models improve the classification accuracy and achieve better performance than standalone models. An accuracy of 97.91% was achieved using a combination of three models which outperforms state-of-the-art techniques.