Numerous studies have affirmed that deep learning models can facilitate early diagnosis of lesions in endoscopic images. However, the lack of available datasets stymies advancements in research on nasal endoscopy, and existing models fail to strike a good trade-off between model diagnosis performance, model complexity and parameters size, rendering them unsuitable for real-world application. To bridge these gaps, we created the first large-scale nasal endoscopy dataset, named 7-NasalEID, comprising 11,352 images that contain six common nasal diseases and normal samples. Subsequently, we proposed U-SEANNet, an innovative U-shaped architecture, underpinned by depth-wise separable convolution. Moreover, to enhance its capacity for detecting nuanced discrepancies in input images, U-SEANNet employs the Global-Local Channel Feature Fusion module, enabling it to utilize salient channel features from both global and local contexts. To demonstrate U-SEANNet's potential, we benchmarked U-SEANNet against seventeen modern architectures through five-fold cross-validation. The experimental results show that U-SEANNet achieves a commendable accuracy of 93.58%. Notably, U-SEANNet's parameters size and GFLOPs are only 0.78M and 0.21, respectively. Our findings suggest U-SEANNet is the state-of-the-art model for nasal diseases diagnosis in endoscopic images.

翻译：众多研究已证实，深度学习模型能够促进内镜图像中病变的早期诊断。然而，可用数据集的缺乏阻碍了鼻内镜研究的进展，且现有模型在诊断性能、模型复杂度和参数量之间未能实现良好权衡，导致其不适用于实际应用。为弥补这些不足，我们构建了首个大规模鼻内镜数据集，命名为7-NasalEID，包含11352张图像，涵盖六种常见鼻部疾病及正常样本。随后，我们提出U-SEANNet，一种创新的U形架构，其核心基于深度可分离卷积。此外，为增强其对输入图像中细微差异的检测能力，U-SEANNet采用全局-局部通道特征融合模块，使其能够从全局和局部上下文中利用显著的通道特征。为展示U-SEANNet的潜力，我们通过五折交叉验证将其与十七种现代架构进行基准对比。实验结果表明，U-SEANNet达到了93.58%的优异准确率。值得注意的是，U-SEANNet的参数量和GFLOPs分别仅为0.78M和0.21。我们的研究结果表明，U-SEANNet是用于内镜图像中鼻部疾病诊断的最先进模型。