Transfer Learning: Pre-trained VGG16 Architecture for Chest X-Ray Classification

Abstract

This thesis considers the task of thorax disease classification on Chest X-Ray images using transfer learning. The thorax or chest is a part of the anatomy of humans and various other animals located between the neck and the abdomen. The thorax contains organs including the heart, lungs, and thymus gland, as well as muscles and various other internal structures. Transfer learning from natural image datasets, particularly ImageNet, using models (VGG16, DenseNet, GoogLeNet etc.) and corresponding pretrained weights are used for deep learning applications to medical imaging. In this thesis, VGG16 network, which is pretrained on ImageNet data is explored. In Chest X-Ray14 dataset there are localized areas which are signs of abnormalities, whereas in ImageNet dataset, there is often a clear global subject of the image. Pretrained VGG16 had 1000 nodes in the output layer, one for each class. We change it to 14 nodes, one for each pathology: Atelectasis, Cardiomegaly, Effusion, Infiltration, Mass, Nodule, Pneumonia, Pneumothorax, Consolidation, Edema,Emphysema, Fibrosis, Pleural_Thickening, Hernia. We experiment with the strategy that CNN should act as a feature extractor. A performance evaluation shows that transfer offers little benefit to performance. We plot Receiver Operating Characteristic (ROC) curve for each of the pathologies. The area under the roc curve (AUROC) is calculated for each class. Average AUROC is calculated by taking the mean of all the classes. The average AUROC of our model is 0.715.