Utilizing deep learning (DL) models to improve the early diagnosis of nasal diseases from nasal endoscopic images holds paramount importance. However, the lack of available datasets stymies advancements in this field. Furthermore, existing models fail to strike a good trade-off between model diagnosis performance, model complexity and parameter size, rendering them unsuitable for practical application. To bridge these gaps, we created the first large-scale nasal endoscopy dataset, named 7-NasEID, comprising 11,352 images that span six nasal diseases and normal samples. Building on this, we proposed U-SEANNet, an innovative architecture, underpinned by depth-wise separable convolutions. Additionally, to augment its discernment capabilities for subtle variations in input images, we further proposed the Global-Local Channel Feature Fusion Module, enabling the U-SEANNet to focus salient channel features from both global and local contexts. Notably, U-SEANNet's parameter size and GFLOPs are only 0.78M and 0.21, respectively. Employing the 7-NasalEID, we conducted the five-fold cross-validation on U-SEANNet, juxtaposing its performance against seventeen renowned architectures. The experimental results suggest U-SEANNet as the state-of-the-art (SOTA) model, achieves an accuracy of 93.58%, sensitivity of 90.17%, and specificity of 91.27%. These findings demonstrate U-SEANNet's prodigious potential for diagnosing nasal diseases in practical use, providing the development of efficacy nasal diseases diagnosis tools with a new insight.

利用深度学习模型从鼻内窥镜图像中提高鼻部疾病早期诊断的重要性，通过创建具有11352张图片的第一个大规模鼻内窥镜图像数据集（7-NasEID），提出了一种创新的U-SEANNet架构，该架构基于深度可分离卷积。实验结果表明U-SEANNet模型是最先进的模型，具有93.58％的准确率，90.17％的灵敏度和91.27％的特异度。

U-SEANNet：一种简单高效且实用的U形网络用于从鼻内窥镜图像诊断鼻部疾病