Eyelume: Vision Transformer-based Pupil Segmentation for Computer Vision Syndrome Detection

Abstract: Computer Vision Syndrome (CVS) has emerged as a critical health concern in the digital era due to prolonged exposure to screens, resulting in eye strain, dryness, and blurred vision. Traditional diagnostic methods rely on clinical examination, which can be invasive, time-consuming, and inaccessible for frequent monitoring. This paper introduces Eyelume, a real-time CVS detection and monitoring system that leverages Vision Transformers (ViT) for accurate pupil segmentation and pupillometry analysis. Unlike Convolutional Neural Networks (CNNs), ViTs capture global dependencies within visual data, offering robustness against low-quality and noisy eye images. The system enables users to upload eye images via a web interface, validates input quality, and computes pupil size variations to identify abnormal responses linked to CVS. Experimental evaluations demonstrate a segmentation accuracy of 99.6%, proving Eyelume’s potential as a non-invasive, accessible, and effective tool for early CVS detection and digital eye health monitoring [1][2].

Keywords: Computer Vision Syndrome, Pupil Segmentation, Vision Transformers, Pupillometry, Digital Eye Health.