# Machine Learning for Cataract Classification and Grading on Ophthalmic Imaging Modalities: A Survey

arXiv: https://arxiv.org/abs/2012.04830 · HTML: https://mlsanity.com/paper/2012.04830/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Dec 2020.

## Built on by (most cited first, 7 of 7)

- [Degradation-invariant Enhancement of Fundus Images via Pyramid Constraint Network](https://mlsanity.com/paper/2210.09606/) (2022)
- [Structure-consistent Restoration Network for Cataract Fundus Image Enhancement](https://mlsanity.com/paper/2206.04684/) (2022)
- [Hard Exudate Segmentation Supplemented by Super-Resolution with Multi-scale Attention Fusion Module](https://mlsanity.com/paper/2211.09404/) (2022)
- [Medical Image Registration and Its Application in Retinal Images: A Review](https://mlsanity.com/paper/2403.16502/) (2024)
- [Adaptive Confidence-Wise Loss for Improved Lens Structure Segmentation in AS-OCT](https://mlsanity.com/paper/2508.08705/) (2025)
- [Pathology Context Recalibration Network for Ocular Disease Recognition](https://mlsanity.com/paper/2512.24066/) (2025)
- [CFCH: Coarse-Fine Collaborative Hierarchical Learning for Anterior Segment Disease Analysis](https://mlsanity.com/paper/2609.32559/) (2026)

## Abstract (arXiv)

Cataracts are the leading cause of visual impairment and blindness globally. Over the years, researchers have achieved significant progress in developing state-of-the-art machine learning techniques for automatic cataract classification and grading, aiming to prevent cataracts early and improve clinicians' diagnosis efficiency. This survey provides a comprehensive survey of recent advances in machine learning techniques for cataract classification/grading based on ophthalmic images. We summarize existing literature from two research directions: conventional machine learning methods and deep learning methods. This survey also provides insights into existing works of both merits and limitations. In addition, we discuss several challenges of automatic cataract classification/grading based on machine learning techniques and present possible solutions to these challenges for future research.