  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.
