论文标题
通过视觉变压器微调精确出发性麻疹检测
Accurate Measles Rash Detection via Vision Transformer Fine-Tuning
论文作者
论文摘要
麻疹是一种高度传染性疾病，在2025年成功进行了数十年的成功疫苗接种运动后，在2000年被宣布消除，其中有1,356例确认的病例据报道，据报道，截至2025年8月5日，据报道，鉴于其易感人群迅速传播，快速可靠的诊断系统对于早期预防和遏制至关重要。在这项工作中，我们应用了转移学习，以微调验证的数据有效图像变压器（DEIT）模型，以区分麻疹与其他皮肤状况。在对分类头进行调整在多样化，精选的皮疹图像数据集上后，DEIT模型的平均分类精度为95.17％，精度为95.06％，召回95.17％，F1评分为95.03％，在准确的小节中表现出高度有效性的效果，以发现有助于探测爆发爆发的能力。我们还将DEIT模型与卷积神经网络进行了比较，并讨论了未来研究的方向。
Measles, a highly contagious disease declared eliminated in the United States in 2000 after decades of successful vaccination campaigns, resurged in 2025, with 1,356 confirmed cases reported as of August 5, 2025. Given its rapid spread among susceptible individuals, fast and reliable diagnostic systems are critical for early prevention and containment. In this work, we applied transfer learning to fine-tune a pretrained Data-efficient Image Transformer (DeiT) model for distinguishing measles rashes from other skin conditions. After tuning the classification head on a diverse, curated skin rash image dataset, the DeiT model achieved an average classification accuracy of 95.17%, precision of 95.06%, recall of 95.17%, and an F1-score of 95.03%, demonstrating high effectiveness in accurate measles detection to aid outbreak control. We also compared the DeiT model with a convolutional neural network and discussed the directions for future research.
