MTCD: Cataract Detection via Near Infrared Eye Images

TL;DR

This paper introduces a novel deep learning approach for cataract detection using low-cost near-infrared eye images, addressing resource limitations in traditional diagnosis methods.

Contribution

The study presents a new algorithm leveraging NIR images and deep learning for accurate, cost-effective cataract detection, which has not been explored before.

Findings

High classification accuracy achieved on cataract dataset

Effective segmentation of non-ideal eye boundaries

Cost-effective method using existing iris recognition hardware

Abstract

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.