Iris Disease Detection using Convolutional Neural Network
Amadou Mouctar Balde, Megha Chhabra, Kiran Kumar Ravulakollu, Mayank Kumar Goyal, Ruchi Agarwal, Ritu Dewan · 2022
It is essential to provide biometric security in most authentication and identification scenarios. The design of a pragmatic user authentication system is vital to provide detection. Iris recognition is considered the most reliable biometric identification due to its steady and extraordinary variation in texture. Iris recognition uses unique patterns to identify those who require a high level of protection. This article examines an effective strategy for facilitating feature extraction and categorization using a convolutional neural network (CNN) to improve recognition efficiency. This present work purposes to create a model that can detect eye disorders such as diabetes, hypertension, glaucoma, myopia, and cataracts. The proposed technique has been effectively applied, and the experimental assessment using iris photos from the casia database demonstrates the performance of the proposed technique.