Paper Key : IRJ************126
Author: Tejaswini.g
Date Published: 08 Nov 2024
Abstract
Keratoconus (KC) is a progressive, non-inflammatory eye disorder that thins and bulges the cornea, distorting vision and potentially leading to blindness if undetected. Early detection is critical for effective treatment and management. With advances in medical imaging and machine learning (ML), automated keratoconus detection has gained momentum as a research focus. This paper reviews current ML techniques in KC detection, analyzes performance metrics, and proposes a novel framework for KC detection using deep learning. Experimental results show that our approach achieves high accuracy in distinguishing KC from non-KC corneal scans, with potential implications for real-world applications in clinical settings.