Comparing pre-trained models through classifying lung conditions using chest x-ray

In classifying chest X-rays (CXR), machine learning, particularly transfer learning, has been widely implemented and has demonstrated an excellent range of accuracy. In this study, ResNet-50, ResNet-101, and VGG16, three popular transfer learning models, were compared in a CXR classification task. This study used a dataset containing CXRs of 3616 COVID- 19-positive cases, CXRs of 1345 viral pneumonia cases, CXRs of 6012 lung opacity (non-Covid lung infection) cases, as well as CXRs of 10,192 normal individuals and corresponding lung masks. The study used Keras and TensorFlow to import the pre-trained models and compare them using the exact same training sets, testing sets, and validation sets. The ResNet-50 achieves a classification accuracy of 90%, the ResNet-101 achieves a classification accuracy of 91%, and the VGG16 achieves a classification accuracy of 89%. This research reached the conclusion that ResNet-101 performs better in such image multiclassification tasks. We speculate that this is because ResNet-101 was trained based on residual learning which is easy to optimize.