文章：

基于移动设备的口腔癌图像分类：应用Vision Transformer与Swin Transformer模型

Classification of Mobile-Based Oral Cancer Images Using the Vision Transformer and the Swin Transformer

原文发布日期：29 February 2024

DOI: 10.3390/cancers16050987

类型: Article

开放获取: 是

英文摘要：

Oral cancer, a pervasive and rapidly growing malignant disease, poses a significant global health concern. Early and accurate diagnosis is pivotal for improving patient outcomes. Automatic diagnosis methods based on artificial intelligence have shown promising results in the oral cancer field, but the accuracy still needs to be improved for realistic diagnostic scenarios. Vision Transformers (ViT) have outperformed learning CNN models recently in many computer vision benchmark tasks. This study explores the effectiveness of the Vision Transformer and the Swin Transformer, two cutting-edge variants of the transformer architecture, for the mobile-based oral cancer image classification application. The pre-trained Swin transformer model achieved 88.7% accuracy in the binary classification task, outperforming the ViT model by 2.3%, while the conventional convolutional network model VGG19 and ResNet50 achieved 85.2% and 84.5% accuracy. Our experiments demonstrate that these transformer-based architectures outperform traditional convolutional neural networks in terms of oral cancer image classification, and underscore the potential of the ViT and the Swin Transformer in advancing the state of the art in oral cancer image analysis.

摘要翻译： 

口腔癌作为一种普遍且快速增长的恶性疾病，已成为全球重要的健康问题。早期准确诊断对于改善患者预后至关重要。基于人工智能的自动诊断方法在口腔癌领域已展现出良好前景，但在实际诊断场景中的准确性仍有待提升。近年来，视觉变换器（ViT）在多项计算机视觉基准任务中表现优于传统卷积神经网络模型。本研究探讨了变换器架构的两种前沿变体——视觉变换器与Swin变换器在移动端口腔癌图像分类应用中的有效性。预训练的Swin变换器模型在二分类任务中达到88.7%的准确率，较ViT模型提升2.3%，而传统卷积网络模型VGG19和ResNet50的准确率分别为85.2%和84.5%。实验结果表明，基于变换器的架构在口腔癌图像分类任务中优于传统卷积神经网络，凸显了ViT与Swin变换器在推进口腔癌图像分析技术发展方面的潜力。

原文链接：

Classification of Mobile-Based Oral Cancer Images Using the Vision Transformer and the Swin Transformer