Publications

Abstract : Glaucoma is one of the foremost causes that result in irreversible blindness. World Health Organisation (WHO) has estimated that around 4.5 million people are blind due to glaucoma. This vast number is because more than 90% of glaucoma cases are undiagnosed. Early-stage glaucoma detection system helps to identify the disease and prevent it from further blindness. The proposed system analyzed and visualized the eye movement patterns of glaucoma-affected participants using machine learning algorithms. The model collected eye-gaze patterns using an eye-tracking sensor and visualized them based on different eye gaze features. The model classified glaucoma and normal based on eye gaze data. The proposed model has incorporated different visualization techniques to understand how eye gaze patterns of normal and glaucoma differ while performing screen-based tasks such as Free-Viewing Task and Image Searching Task. The proposed system has also incorporated an interactive dashboard using Microsoft PowerBI Software.