Deep Classifier Fusion-based Multi-Classification of Chest X-rays for COVID-19 Detection

In the last three years, The COVID-19 pandemic has placed immense strain on healthcare systems worldwide. Imaging techniques, such as Chest X-Ray (CXR) images, play an essential role in diagnosing COVID-19. Recently, Computer Aided Diagnosis systems based on Deep Learning have been widely used to identify COVID-19 from other upper respiratory diseases such as viral pneumonia. To enhance COVID-19 diagnosis using CXR images, this article proposes a methodology that combines three deep learning models renowned for their depth and feature extraction capabilities: ResNet, DenseNet, and VGG. The model’ strengths are harnessed using Aggregation techniques such as stacking and voting (majority, weighted, average), to improve prediction accuracy. Moreover, the used dataset was subjected to preprocessing, balancing, and augmentation techniques, including rotation and zooming, to ensure robust recognition of CXR images, even in challenging positions. Experimental results demonstrate that the combination of model ensembles and enhanced dataset leads to superior accuracy in diagnosing COVID-19, with an impressive 97.95% accuracy, 98.2% F1 score, and 99.9% AUC-ROC. This approach not only outperforms individual models but also offers a more reliable and efficient solution for COVID-19 detection, particularly in complex cases where symptoms overlap with other respiratory illnesses.