# Explainable Deep Learning for Cataract Detection in Retinal Images: A Dual-Eye and Knowledge Distillation Approach

arXiv: https://arxiv.org/abs/2509.22696 · HTML: https://mlsanity.com/paper/2509.22696/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 33 references that are in the map.
- First posted on arXiv 20 Sep 2025.

## Builds on (most cited first, 13 of 13)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [Distilling the Knowledge in a Neural Network](https://mlsanity.com/paper/1503.02531/) (2015)
- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [Training data-efficient image transformers & distillation through attention](https://mlsanity.com/paper/2012.12877/) (2020)
- [MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications](https://mlsanity.com/paper/1704.04861/) (2017)
- [Searching for MobileNetV3](https://mlsanity.com/paper/1905.02244/) (2019)
- [Replication study: Development and validation of deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs](https://mlsanity.com/paper/1803.04337/) (2018)
- [Evaluation of a deep learning system for the joint automated detection of diabetic retinopathy and age-related macular degeneration](https://mlsanity.com/paper/1903.09555/) (2019)
- [A CNN-LSTM Combination Network for Cataract Detection using Eye Fundus Images](https://mlsanity.com/paper/2210.16093/) (2022)

## Abstract (arXiv)

Cataract remains a leading cause of visual impairment worldwide, and early detection from retinal imaging is critical for timely intervention. We present a deep learning pipeline for cataract classification using the Ocular Disease Recognition dataset, containing left and right fundus photographs from 5000 patients. We evaluated CNNs, transformers, lightweight architectures, and knowledge-distilled models. The top-performing model, Swin-Base Transformer, achieved 98.58% accuracy and an F1-score of 0.9836. A distilled MobileNetV3, trained with Swin-Base knowledge, reached 98.42% accuracy and a 0.9787 F1-score with greatly reduced computational cost. The proposed dual-eye Siamese variant of the distilled MobileNet, integrating information from both eyes, achieved an accuracy of 98.21%. Explainability analysis using Grad-CAM demonstrated that the CNNs concentrated on medically significant features, such as lens opacity and central blur. These results show that accurate, interpretable cataract detection is achievable even with lightweight models, supporting potential clinical integration in resource-limited settings