Deep Learning-based ENT Endoscopy Image Classification: A Technical Report for ENTRep 2025 Challenge

Abstract

ENT (Ear, Nose, and Throat) endoscopic imaging plays a crucial role in the diagnosis and treatment planning of a wide range of head and neck disorders, yet remains underrepresented in the development of automated image analysis methods. While endoscopic image classification has been extensively explored in various anatomical domains, limited attention has been paid to the ear, nose, and throat (ENT) region. To address this gap, we conduct a systematic study on ENT endoscopy image classification, motivated by our 3rd place solution in the ENTRep Challenge at ACM Multimedia 2025. We train a total of 190 models, spanning 19 representative architectures from both convolutional neural networks (CNNs) and vision transformers (ViTs), under a 10-fold cross-validation setting. In addition, we propose a selective ensemble strategy, which achieves improved classification performance with reduced computational cost. Additionally, we provide an empirical analysis of training strategies, including the effects of pretraining and data augmentation, offering practical insights into effective model design for ENT applications. This work represents a systematic evaluation of ENT endoscopic image classification methods and provides a strong baseline and methodological guidance for future research in this domain.