COVID-19 Detection on Chest X-Ray Images: A comparison of CNN architectures and ensembles
This work addresses the need for rapid COVID-19 screening using medical imaging, but it is incremental as it applies existing CNN methods to a specific dataset.
The paper tackled COVID-19 detection from chest X-ray images by comparing 21 CNN architectures and ensembles, achieving up to 99.25% accuracy and 99.24% F1 score with an ensemble of DenseNet169 models, outperforming prior work on the same dataset.
COVID-19 quickly became a global pandemic after only four months of its first detection. It is crucial to detect this disease as soon as possible to decrease its spread. The use of chest X-ray (CXR) images became an effective screening strategy, complementary to the reverse transcription-polymerase chain reaction (RT-PCR). Convolutional neural networks (CNNs) are often used for automatic image classification and they can be very useful in CXR diagnostics. In this paper, 21 different CNN architectures are tested and compared in the task of identifying COVID-19 in CXR images. They were applied to the COVIDx8B dataset, a large COVID-19 dataset with 16,352 CXR images coming from patients of at least 51 countries. Ensembles of CNNs were also employed and they showed better efficacy than individual instances. The best individual CNN instance results were achieved by DenseNet169, with an accuracy of 98.15% and an F1 score of 98.12%. These were further increased to 99.25% and 99.24%, respectively, through an ensemble with five instances of DenseNet169. These results are higher than those obtained in recent works using the same dataset.