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.Summary
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