# A Deep Learning Interpretable Classifier for Diabetic Retinopathy Disease Grading

arXiv: https://arxiv.org/abs/1712.08107 · HTML: https://mlsanity.com/paper/1712.08107/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Dec 2017.

## Built on by (most cited first, 9 of 9)

- [Applications of Deep Learning in Fundus Images: A Review](https://mlsanity.com/paper/2101.09864/) (2021)
- [Negative Evidence Matters in Interpretable Histology Image Classification](https://mlsanity.com/paper/2201.02445/) (2022)
- [Enhancing Diabetic Retinopathy Diagnosis: A Lightweight CNN Architecture for Efficient Exudate Detection in Retinal Fundus Images](https://mlsanity.com/paper/2408.06784/) (2024)
- [Deep Supervised Information Bottleneck Hashing for Cross-modal Retrieval based Computer-aided Diagnosis](https://mlsanity.com/paper/2205.08365/) (2022)
- [XDR-LVLM: An Explainable Vision-Language Large Model for Diabetic Retinopathy Diagnosis](https://mlsanity.com/paper/2508.15168/) (2025)
- [Designing an Improved Deep Learning-based Model for COVID-19 Recognition in Chest X-ray Images: A Knowledge Distillation Approach](https://mlsanity.com/paper/2301.02735/) (2023)
- [Improving the classification of extreme classes by means of loss regularisation and generalised beta distributions](https://mlsanity.com/paper/2407.12417/) (2024)
- [DRetNet: A Novel Deep Learning Framework for Diabetic Retinopathy Diagnosis](https://mlsanity.com/paper/2509.01072/) (2025)
- [Self-Explainability in Self-Adaptive and Self-Organising Systems: Status and Research Directions](https://mlsanity.com/paper/2606.09568/) (2026)

## Abstract (arXiv)

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.