用深度迁移学习提升新冠肺影像诊断准确率，模型表现接近完美。
COVID-19 Pneumonia Diagnosis Using Medical Images: Deep Learning-Based Transfer Learning Approach
- 采用多种先进CNN模型，通过迁移学习从胸部X光和CT图中自动识别新冠肺炎
- DenseNet121模型在测试中达到98%准确率，各项指标均超97%
- 适合医疗资源有限地区快速部署，对新变异株也具较强适应性
SARS-CoV-2作为新冠肺炎的致病源，因其高传播性和不断演变的变异株仍是全球健康威胁。截至2025年2月中旬，全球检测阳性率升至11%，为六个月来最高水平，尽管疫苗接种广泛开展。新型变异株表现出更强的宿主细胞结合能力，增加了感染性和诊断复杂度。本研究评估了深度迁移学习在快速、精准且抗变异的COVID-19影像诊断中的有效性，重点关注可扩展性与可及性。我们开发了一个自动化检测系统，使用包括VGG16、ResNet50、ConvNetXtTiny、MobileNet、NASNetMobile和DenseNet121在内的多种先进CNN模型，从胸片和CT图像中识别COVID-19。在所有模型中，DenseNet121在胸片和CT图像上的表现最佳，准确率达98%，精确率为96.9%，召回率为98.9%，F1分数为97.9%，AUC得分为99.8%，表明其在正负病例检测上高度一致可靠。混淆矩阵显示极少出现假阳性和假阴性，凸显该模型在真实诊疗场景中的稳健性。
原文摘要 · Abstract (English)
SARS-CoV-2, the causative agent of COVID-19, remains a global health concern due to its high transmissibility and evolving variants. Although vaccination efforts and therapeutic advancements have mitigated disease severity, emerging mutations continue to challenge diagnostics and containment strategies. As of mid-February 2025, global test positivity has risen to 11%, marking the highest level in over six months despite widespread immunization efforts. Newer variants demonstrate enhanced host cell binding, increasing both infectivity and diagnostic complexity. This study evaluates the effectiveness of deep transfer learning in delivering rapid, accurate, and mutation-resilient COVID-19 diagnosis from medical imaging, with a focus on scalability and accessibility. We developed an automated detection system using state-of-the-art CNNs, including VGG16, ResNet50, ConvNetXtTiny, MobileNet, NASNetMobile, and DenseNet121 among others, to detect COVID-19 from chest X-ray and CT images. Among all the models evaluated, DenseNet121 emerged as the best-performing architecture for COVID-19 diagnosis using CT and X-ray images. It achieved an impressive accuracy of 98%, with 96.9% precision, 98.9% recall, 97.9% F1-score and 99.8% AUC score, indicating a high degree of consistency and reliability in both detecting positive and negative cases. The confusion matrix showed minimal false positives and false negatives, underscoring the model's robustness in real-world diagnostic scenarios.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。