Researchers have developed a real-time video-based system for detecting squint and cataract using computer vision. The system employs a media-pipe face-mesh model to extract ocular features for squint classification and analyzes grayscale intensity for cataract detection. This low-cost framework, deployable with standard cameras, achieved high accuracy rates of 98.39% for squint detection and 96.90% for cataract classification. The technology aims to improve web accessibility for individuals with visual impairments by enabling adaptive user interfaces. AI
IMPACT This research could lead to more accessible web interfaces and assistive technologies for individuals with visual impairments.
RANK_REASON The cluster describes a research paper detailing a new method for detecting medical conditions using computer vision.
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