基于ViT与CNN架构的胸部X光图像COVID-19重症程度诊断

Diagnosing COVID-19 Severity from Chest X-Ray Images Using ViT and CNN Architectures

February 23, 2025

作者: Luis Lara, Lucia Eve Berger, Rajesh Raju, Shawn Whitfield

cs.AI

摘要

新冠疫情对医疗资源造成了巨大压力，并引发了关于机器学习如何减轻医生负担、辅助诊断的广泛讨论。胸部X光片（CXRs）被用于新冠肺炎的诊断，但鲜有研究基于CXRs预测患者病情的严重程度。在本研究中，我们通过整合三个来源的数据，构建了一个大规模的新冠病情严重程度数据集，并探究了基于ImageNet和CXR预训练模型以及视觉Transformer（ViTs）在病情严重程度回归与分类任务中的有效性。其中，预训练的DenseNet161模型在三类病情严重程度预测问题上表现最佳，整体准确率达到80%，在轻度、中度和重度病例上的准确率分别为77.3%、83.9%和70%。而ViT在回归任务中取得了最优结果，其预测的病情严重程度评分与放射科医生的评分相比，平均绝对误差为0.5676。本项目的源代码已公开。

English

The COVID-19 pandemic strained healthcare resources and prompted discussion about how machine learning can alleviate physician burdens and contribute to diagnosis. Chest x-rays (CXRs) are used for diagnosis of COVID-19, but few studies predict the severity of a patient's condition from CXRs. In this study, we produce a large COVID severity dataset by merging three sources and investigate the efficacy of transfer learning using ImageNet- and CXR-pretrained models and vision transformers (ViTs) in both severity regression and classification tasks. A pretrained DenseNet161 model performed the best on the three class severity prediction problem, reaching 80% accuracy overall and 77.3%, 83.9%, and 70% on mild, moderate and severe cases, respectively. The ViT had the best regression results, with a mean absolute error of 0.5676 compared to radiologist-predicted severity scores. The project's source code is publicly available.