Cataracts are among the leading diseases that cause blindness around the world, emerging as one of the most com- mon eye diseases. Traditional approaches for cataract diagnosis depends on the manual examination of patients by eye specialist doctors, which can be time consuming. Early detection and timely intervention are crucial to prevent vision loss. In recent years, more advanced deep learning algorithms such as Convolutional Neural Networks, VGG16, MobileNet, etc., are introduced into the medical imaging field to address these limitations. these algorithms provide a good advantage in fully automated cataract detection with high accuracy, rapidity and dependability. In this review paper, deep learning for cataract detection has been high- lighted by comparing different algorithms for cataract detection. Alongside this, we discuss the advantages and disadvantages as well as the challenges and future directions of these algorithms. This paper aims to provide an introduction on the application of deep learning in cataract detection, making it accessible for both researchers and non-specialists interested in AI-driven healthcare solutions.
Thakor et al. (Tue,) studied this question.