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

TL;DR

This paper introduces a semi-supervised ResNet-based model for COVID-19 X-ray image classification that effectively utilizes limited labeled data and addresses class imbalance, achieving promising results.

Contribution

It presents a novel semi-supervised deep learning approach with a weighted loss to improve COVID-19 image classification with scarce labeled data.

Findings

Achieves high accuracy with few labeled images

Effectively handles class imbalance in datasets

Outperforms some existing methods on COVIDx dataset

Abstract

Coronavirus disease 2019 (COVID-19) is an ongoing global pandemic in over 200 countries and territories, which has resulted in a great public health concern across the international community. Analysis of X-ray imaging data can play a critical role in timely and accurate screening and fighting against COVID-19. Supervised deep learning has been successfully applied to recognize COVID-19 pathology from X-ray imaging datasets. 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 such as COVID-19 outbreak, especially in the early stage of the outbreak. To address this challenge, this paper proposes a two-path semi-supervised deep learning model, ssResNet, based on Residual Neural Network (ResNet) for COVID-19 image classification, where two paths refer to a supervised path and an unsupervised path, respectively. Moreover, we design a weighted supervised loss that assigns higher weight for the minority classes in the training process to resolve the data imbalance. Experimental results on a large-scale of X-ray image dataset COVIDx demonstrate that the proposed model can achieve promising performance even when trained on very few labeled training images.