Abstract: Cataract is one of the foremost common eye maladies that cause visual impedance. Exact and opportune discovery of cataract is perfect way" the most perfect way to oversee the hazard and anticipate visual disability. As of late, cataract discovery frameworks based on counterfeit information have pulled in inquire about consideration. In this paper, we propose a novel profound neural framework, Cataract Net, for programmed discovery of cataract in fundus pictures. The misfortune and actuation capacities are tuned to plan the framework with less components, less preparing parameters and layers. Robotized conclusion of eye diseases using machine and profound learning models is getting to be increasingly common. Glaucoma, cataracts, diabetic retinopathy, astigmatism and age-related macular degeneration are common eye diseases that can cause genuine hurt. It is vital to capture eye contaminations early to maintain a strategic distance from genuine results. Early conclusion of eye maladies is fundamental for successful treatment.
Keywords: Cataract detection, eye disease, machine learning, glaucoma, deep learning.
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DOI:
10.17148/IJARCCE.2024.13657
