# A Deep Learning Interpretable Classifier for Diabetic Retinopathy Disease Grading Source: [A Deep Learning Interpretable Classifier for Diabetic Retinopathy Disease Grading]() Collection index: [llms.txt]() Data updated: 2025-10-25T21:32:37.817Z Authors: Jordi de la Torre, Aida Valls, Domenec Puig Published: 2017-12-21T17:40:32.000Z ## Abstract Deep neural network models have been proven to be very successful in image classification tasks, also for medical diagnosis, but their main concern is its lack of interpretability. They use to work as intuition machines with high statistical confidence but unable to give interpretable explanations about the reported results. The vast amount of parameters of these models make difficult to infer a rationale interpretation from them. In this paper we present a diabetic retinopathy interpretable classifier able to classify retine images into the different levels of disease severity and of explaining its results by assigning a score for every point in the hidden and input space, evaluating its contribution to the final classification in a linear way. The generated visual maps can be interpreted by an expert in order to compare its own knowledge with the interpretation given by the model. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()