LsEyeNet: A Hybrid Deep Feature Fusion Framework for Multi-Class Ophthalmic Disease Recognition from Retinal Fundus Images

LsEyeNet, a hybrid deep feature fusion model is proposed for multi-class ophthalmic disease recognition from fundus images. The study evaluates supervised and self-supervised deep learning models in a comprehensive fashion in a common framework. The proposed framework using combined complementary feature representations of ConvNeXtBase and EfficientNetB3 attains improved classification results, such as classification accuracy of 97.80% and F1-score of 0.96. It also investigates the potential of self-supervised learning using SimCLR and shows the advantage of feature fusion in building a reliable, efficient and accurate AI supported ophthalmic screening framework.