Evaluation of classification performance for six types of fundus diseases in OCT images based on multi-source training strategy

Multi-source data fusion serves as an effective approach to enhance the robustness of OCT multi-category classification models, and it can notably strengthen the recognition capability for certain diseases in particular. This study not only verifies the universal benefits of this strategy but also reveals the critical impact of model selection on the transfer learning effect.