Innovative Approaches in Cataract Detection: Exploring Transfer Learning and Image Segmentation Techniques

The natural lens of the eye gets foggy in cataracts, a common eye ailment that seriously harms vision and overall eye health. As the disease gets worse, it may block light from reaching the retina, developing clouded vision and possibly blindness if treatment has not been given. Effective medications for preserving the eye’s clarity rely on early detection of cataracts. Our research aims to use innovative methods, especially deep learning and convolutional neural networks (CNNs), to deal with this difficulty. The creation of a computer-based system that may detect cataracts in eye pictures is made feasible by these precise algorithms. Through the use of CNNs, the system is able to identify complex patterns in these images and identify between eyes that are healthy and those that have cataracts. Drawing up an unbiased and accurate way for the timely identification of cataracts is the core objective of our research. An aid like this would improve overall eye care in addition to immediate medical care for this problem. By incorporating techniques from deep learning, we expect to discover a sustainable solution that can help medical professionals treat cataracts as quickly as possible, limiting the detrimental impact on their vision and brightening the lives of those who are affected.