  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.
