A comparative analysis of eleven neural networks architectures for small datasets of lung images of COVID-19 patients toward improved clinical decisions

Abstract

The 2019 novel severe acute respiratory syndrome coronavirus 2-SARS-CoV2, commonly known as COVID-19, is a highly infectious disease that has endangered the health of many people around the world. COVID-19, which infects the lungs, is often diagnosed and managed using X-ray or computed tomography (CT) images. For such images, rapid and accurate classification and diagnosis can be performed using deep learning methods that are trained using existing neural network models. However, at present, there is no standardized method or uniform evaluation metric for image classification, which makes it difficult to compare the strengths and weaknesses of different neural network models. This paper used eleven well-known convolutional neural networks, including VGG-16, ResNet-18, ResNet-50, DenseNet-121, DenseNet-169, Inception-v3, Inception-v4, SqueezeNet, MobileNet, ShuffeNet, and EfficientNet-b0, to classify and distinguish COVID-19 and non-COVID-19 lung images. These eleven models were applied to different batch sizes and epoch cases, and their overall performance was compared and discussed. The results of this study can provide decision support in guiding research on processing and analyzing small medical datasets to understand which model choices can yield better outcomes in lung image classification, diagnosis, disease management and patient care.