Chest radiography has become the modality of choice for diagnosing pneumonia. However, analyzing chest X-ray images may be tedious, time-consuming and requiring expert knowledge that might not be available in less-developed regions. therefore, computer-aided diagnosis systems are needed. Recently, many classification systems based on deep learning have been proposed. Despite their success, the high development cost for deep networks is still a hurdle for deployment. Deep transfer learning (or simply transfer learning) has the merit of reducing the development cost by borrowing architectures from trained models followed by slight fine-tuning of some layers. Nevertheless, whether deep transfer learning is effective over training from scratch in the medical setting remains a research question for many applications. In this work, we investigate the use of deep transfer learning to classify pneumonia among chest Xray images. Experimental results demonstrated that, with slight fine-tuning, deep transfer learning brings performance advantage over training from scratch. Three models, ResNet-50, Inception V3 and DensetNet121, were trained separately through transfer learning and from scratch. The former can achieve a 4.1% to 52.5% larger area under the curve (AUC) than those obtained by the latter, suggesting the effectiveness of deep transfer learning for classifying pneumonia in chest X-ray images.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.