Semi-supervised Learning for COVID-19 Image Classification via ResNet

Abstract

INTRODUCTION: Coronavirus disease 2019 (COVID-19) is an ongoing global pandemic in over 200 countries and territories. Analysis of X-ray imaging data can play a critical role in timely and accurate screening and fighting against COVID-19. OBJECTIVES: Supervised deep learning dominates COVID-19 pathology data analytics. However, it requires a substantial amount of annotated X-ray images to train models, which is often not applicable to data analysis for emerging events. METHODS: The proposed model with two paths is built based on Residual Neural Network for COVID-19 image classification to reduce labeling efforts, where the two paths refer to a supervised path and an unsupervised path, respectively. RESULTS: Experimental results demonstrate that the proposed model can achieve promising performance even when trained on very few labeled training image. CONCLUSION: The proposed model can reduces the efforts of building deep learning models significantly for COVID-19 image classification.

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“…In addition, to address the shortcomings of training data, Castiglioni et [21]. Moreover, Lucy et al [22] developed two-path semi-supervised deep learning model to implement COVID-19 classification by using huge amounts of unlabeled data.…”