A Siamese neural network-based diagnosis of COVID-19 using chest X-rays

Abstract

Radiological findings play an essential and complementary role in diagnosing Covid-19, assessing its severity, and managing its patients. Artificial intelligence technology based on medical imaging, which has made exciting developments by being applied in many areas, has become an area of interest for the rapid and accurate detection of the disease in the fight against the Covid-19 pandemic. The main difficulty is the inability to obtain a large dataset size with quality and standard images that neural networks need to perform well. Aiming at this problem, this study proposes a Siamese neural network-based deep learning framework for accurate diagnostics of Covid-19 using chest X-ray (CXR) images. The pre-trained VGG16 architecture, based on the transfer learning approach, forms the backbone of the Siamese neural network. The outputs of the backbones are joined together by a merging layer, and then the output passes through a fully connected layer. Based on this structure, category-aware Siamese-based models are produced for each class. The predictions from the models are combined using a voting mechanism to reduce the possibility of misclassification and to make better decisions. The framework was evaluated using a publicly available dataset for the 4-class classification task for Covid-19 pneumonia, lung opacity, normal, and non-Covid-19 viral pneumonia images. The findings reveal the high discrimination ability of the framework, trained using only 10 images per class in less training time, achieving an average test accuracy of 92%. Our framework, which learns a single Siamese-based pairwise model for each class, effectively captures class-specific features. Additionally, it has the potential to deal with data scarcity and long training time problems in multi-class classification tasks.