电子科技2026，Vol.39Issue(1)：40-46,7.DOI:10.16180/j.cnki.issn1007-7820.2026.01.006
基于欠采样结合半监督学习的肺损伤评级分类
Research on Lung Injury Rating Classification Based on Under-Sampling and Semi-Supervised Learning
摘要
Abstract
Lung ultrasound can assist doctors in evaluating pulmonary lesions through direct or indirect signs,and quickly screen the causes of acute dyspnea,which is beneficial for doctors to better evaluate and manage patients with pulmonary lesions.This study proposes a new automatic lung ultrasound scoring method to achieve a more accurate assessment of lung injury.Random under-sampling preprocessing is adopted to address the class imbalance problem ex-isting in the dataset.A semi-supervised learning method is employed to make better use of the information of the exclu-ded samples.During the training process,CPLE(Cross Pseudo-loss Estimation)is used to select reliable pseudo-la-beled data for training,further improving the performance of the model.The experimental results show that the pro-posed method has better performance on the measured dataset,with an accuracy rate of 78.51％,and improves the classification accuracy of the minority classes.关键词
肺超声/肺损伤评估/深度学习/图像分类/欠采样/半监督学习/交叉伪监督/类别不平衡Key words
lung ultrasound/lung injury assessment/deep learning/image classification/under-sampling/semi-supervised learning/cross-pseudo-supervision/class imbalance分类
信息技术与安全科学引用本文复制引用
黄秋城,张鞠成,黄天海,褚永华,蒋明峰..基于欠采样结合半监督学习的肺损伤评级分类[J].电子科技,2026,39(1):40-46,7.基金项目
浙江省科技厅重点研发项目(2023C03088)Key Research and Development Project of Zhejiang Provincial Science and Technology Department(2023C03088) （2023C03088）
