High-Accuracy Cataract Detection using Enhanced VGG-19 CNN and Adam Optimizer

Among the foremost predominant conditions influencing the eyes are cataracts, which can cause severe visual impedance and, within the worst-case situation, visual impairment. In arrange to relieve these negative results, early discovery and treatment are basic. Due to a need of qualified ophthalmologists, delays may happen from the exorbitant and specialized nature of conventional demonstrative strategies such fundus cameras and opening lights. In this work, we recommend a computerized procedure for identifying cataracts utilizing the VGG-19 convolutional neural network (CNN) show, which has been Adam optimizer-optimized. With the utilize of computerized camera pictures, our strategy looks for to make strides cataract conclusion proficiency and precision. With an F1 score of 0.972, accuracy of 0.96, review of 0.98, and shocking exactness of 97%, the proposed demonstrate performed honourably. These discoveries bolster the model’s adequacy in accurately distinguishing cataracts, giving a workable alternative for early conclusion and bettering persistent results.