Real-Time Cataract Diagnosis with GhostYOLO: A GhostConv-enhanced YOLO Model - Puchakayala Lokesh, Lahari; Poola, Rahul Gowtham; Gorrepati, Leela Prasad; Yellampalli, Siva Sankar (Engineering, Technology & Applied Science Research, 2025)
This study presents GhostYOLO, an enhanced YOLO-based model for cataract detection that incorporates GhostConv layers to offer greater accuracy, faster processing, and less memory consumption for real-time diagnosis. Initially, the performance of YOLO models, namely YOLOv5, YOLOv6, YOLOv7, and YOLOv8, was evaluated using 788 normal and 920 cataract images, with YOLOv8n emerging as the best standard model with excellent precision, speed, and efficiency. GhostYOLO models were developed to further improve speed and accuracy. GhostYoloV8n obtained the highest accuracy, speed, and lowest memory usage, while GhostYoLoV7-tiny also performed well. Incorporating GhostConv layers substantially improved cataract detection, increasing efficiency and real-time usage. Real-time tests using a Jetson Nano board demonstrated its efficiency, with 33.5 FPS in live detection, simplifying diagnosis. GhostYoloV8n, with only 1.6 million parameters, is a small but powerful cataract detection tool that allows for faster and more precise medical intervention. This study highlights the benefits of including GhostConv layers in YOLO models, making cataract diagnosis more accurate, efficient, and scalable for clinical applications.