# MTCD: Cataract Detection via Near Infrared Eye Images

arXiv: https://arxiv.org/abs/2110.02564 · HTML: https://mlsanity.com/paper/2110.02564/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 35 references that are in the map.
- First posted on arXiv 6 Oct 2021.

## Builds on (most cited first, 9 of 9)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [Rethinking the Inception Architecture for Computer Vision](https://mlsanity.com/paper/1512.00567/) (2015)
- [Fully Convolutional Networks for Semantic Segmentation](https://mlsanity.com/paper/1411.4038/) (2014)
- [Auxiliary Tasks in Multi-task Learning](https://mlsanity.com/paper/1805.06334/) (2018)
- [CaDIS: Cataract Dataset for Image Segmentation](https://mlsanity.com/paper/1906.11586/) (2019)
- [SegDenseNet: Iris Segmentation for Pre and Post Cataract Surgery](https://mlsanity.com/paper/1801.10100/) (2018)

## Built on by (most cited first, 3 of 3)

- [Multi-task Explainable Skin Lesion Classification](https://mlsanity.com/paper/2310.07209/) (2023)
- [Predicting Postoperative Intraocular Lens Dislocation in Cataract Surgery via Deep Learning](https://mlsanity.com/paper/2312.03401/) (2023)
- [Video-Based Detection of squint and cataract for accessibility-aware adaptive web interface rendering](https://mlsanity.com/paper/2607.07099/) (2026)

## Abstract (arXiv)

Globally, cataract is a common eye disease and one of the leading causes of blindness and vision impairment. The traditional process of detecting cataracts involves eye examination using a slit-lamp microscope or ophthalmoscope by an ophthalmologist, who checks for clouding of the normally clear lens of the eye. The lack of resources and unavailability of a sufficient number of experts pose a burden to the healthcare system throughout the world, and researchers are exploring the use of AI solutions for assisting the experts. Inspired by the progress in iris recognition, in this research, we present a novel algorithm for cataract detection using near-infrared eye images. The NIR cameras, which are popularly used in iris recognition, are of relatively low cost and easy to operate compared to ophthalmoscope setup for data capture. However, such NIR images have not been explored for cataract detection. We present deep learning-based eye segmentation and multitask network classification networks for cataract detection using NIR images as input. The proposed segmentation algorithm efficiently and effectively detects non-ideal eye boundaries and is cost-effective, and the classification network yields very high classification performance on the cataract dataset.