In this paper, we propose an explainable and interpretable diabetic
retinopathy (ExplainDR) classification model based on neural-symbolic learning.
To gain explainability, a highlevel 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 humanreadable 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.

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