Enhancing Cataract Screening with Deep Learning Techniques

Cataract is one of the primary causes of blindness. It is highly essential to diagnose them properly and in early stages for an efficient treatment procedure. Manual diagnostic methods applied by an ophthalmologist are not only time-consuming but also subjective in nature. This research proposes to develop an accurate and automated cataract diagnosis system by applying the deep learning methodology. The CNNs, including architectures like VGG16 and ResNet, are used to classify the medical eye images with high precision and efficiency. This paper considers several deep learning models to check which one can be the most efficient for the detection of cataract. The study's performance metrics for assessing the models’ dependability include accuracy, sensitivity, and specificity. The study offers a reliable, scalable, and effective method for early disease identification that can be extended into more extensive ophthalmological applications in the future. It also helps integrate artificial intelligence in medical diagnostics.