Many-MobileNet: Multi-Model Augmentation for Robust Retinal Disease Classification

Hao Wang, Wenhui Zhu, Xuanzhao Dong, Yanxi Chen, Xin Li, Peijie Qiu, Xiwen Chen, Vamsi Krishna Vasa, Yujian Xiong, Oana M. Dumitrascu, Abolfazl Razi, Yalin Wang

Abstract: In this work, we propose Many-MobileNet, an efficient model fusion strategy for retinal disease classification using lightweight CNN architecture. Our method addresses key challenges such as overfitting and limited dataset variability by training multiple models with distinct data augmentation strategies and different model complexities. Through this fusion technique, we achieved robust generalization in data-scarce domains while balancing computational efficiency with feature extraction capabilities.

External IDs:dblp:journals/corr/abs-2412-02825