MTCD: Cataract detection via near infrared eye images

• Published in: Computer vision and image understanding Vol. 214; p. 103303
• Main Authors: Tripathi, Pavani, Akhter, Yasmeena, Khurshid, Mahapara, Lakra, Aditya, Keshari, Rohit, Vatsa, Mayank, Singh, Richa
• Format: Journal Article
• Language: English
• Published: Elsevier Inc 01.01.2022
• Subjects: Biometrics; Cataract; Classification; Deep learning; Iris; Multitask Learning; Biometrics; Deep learning; Iris; Cataract; Multitask Learning; Classification
• ISSN: 1077-3142; 1090-235X
• DOI: 10.1016/j.cviu.2021.103303

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. •Propose cataract detection using eye-images captured in near infrared domain.•Multitask deep learning algorithm is proposed for automated cataract detection.•Proposed PyramidNet segmentation, which is computationally inexpensive and yields high accuracy.