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Numerous studies have affirmed that deep learning models can facilitate earlydiagnosis of lesions in endoscopic images. However, the lack of availabledatasets stymies advancements in research on nasal endoscopy, and existingmodels fail to strike a good trade-off between model diagnosis performance,model complexity and parameters size, rendering them unsuitable for real-worldapplication. To bridge these gaps, we created the first large-scale nasalendoscopy dataset, named 7-NasalEID, comprising 11,352 images that contain sixcommon nasal diseases and normal samples. Subsequently, we proposed U-SEANNet,an innovative U-shaped architecture, underpinned by depth-wise separableconvolution. Moreover, to enhance its capacity for detecting nuanceddiscrepancies in input images, U-SEANNet employs the Global-Local ChannelFeature Fusion module, enabling it to utilize salient channel features fromboth global and local contexts. To demonstrate U-SEANNet's potential, webenchmarked U-SEANNet against seventeen modern architectures through five-foldcross-validation. The experimental results show that U-SEANNet achieves acommendable accuracy of 93.58%. Notably, U-SEANNet's parameters size and GFLOPsare only 0.78M and 0.21, respectively. Our findings suggest U-SEANNet is thestate-of-the-art model for nasal diseases diagnosis in endoscopic images.

U-SEANNet: A Simple, Efficient and Applied U-Shaped Network for Diagnosis of Nasal Diseases on Nasal Endoscopic Images