Simplifying Eye Disease Detection: A New Approach

Deep learning models have been making waves in the medical field, especially when it comes to diagnosing eye diseases. These models often focus on boosting accuracy or speed, but accessibility is a growing concern. Early detection is key to preventing vision loss, and that's where new datasets come into play. They help create better neural network models. However, many existing methods are too complex for regular use.In this study, researchers tackled the accessibility issue head-on. They redesigned and optimized the core of the neural network, the convolutional layers. This led to a new model with a novel layer called ArConv. This layer is efficient and suitable for mobile devices. The final model, with only 1. 3 million parameters, outperformed MobileNetV2 in accuracy when tested on the RfMiD dataset. It achieved an impressive 0. 9328 accuracy, compared to MobileNetV2's 0. 9266. This new model opens the door for more accessible and accurate eye disease diagnosis. It shows that simplifying models can lead to better performance, making advanced medical tools more accessible to everyone.