Soft Computing Based Discriminator Model for Eye Disease

This paper presents a novel approach for glaucoma diagnosis using a lightweight convolutional neural network (CNN) architecture optimized for efficiency and accuracy. The model, designed to run effectively on resource-constrained devices, leverages depthwise separable convolutions and advanced image preprocessing to detect glaucomatous features in fundus images. Evaluated on benchmark datasets, the proposed method demonstrates high diagnostic accuracy and computational efficiency, indicating its potential for integration into real-time, accessible glaucoma screening tools, especially in underserved regions.