Explainable Diabetic Retinopathy Classification Based on Neural-Symbolic Learning

In this paper, we propose an explainable diabetic retinopathy (ExplainDR) classification model based on neural-symbolic learning. To gain explainability, a high-level symbolic representation should be considered in decision making. Specifically, we introduce a human-readable symbolic representation, which follows a taxonomy style of diabetic retinopathy characteristics related to eye health conditions to achieve explainability. We then include human-readable features obtained from the symbolic representation in the disease prediction. Experimental results on a diabetic retinopathy classification dataset show that our proposed ExplainDR method exhibits promising performance when compared to that from state-of-the-art methods applied to the IDRiD dataset, while also providing interpretability and explainability.