Single Board Computer-Based Cataract And Glaucoma Classification And Detection System Using Deep Convolutional Neural Network.

This study introduces an innovative strategy for the identification and categorization of cataract and glaucoma utilizing a Single Board Computer (SBC)-based platform coupled with Deep Convolutional Neural Networks (CNNs). By harnessing the power of SBCs, our system facilitates rapid computational analysis of ocular images, streamlining the diagnostic process for timely intervention. Our methodology leverages CNNs to autonomously discern distinctive features from retinal images, ensuring precise and reliable classification of cataract and glaucoma cases. Through the fusion of advanced image processing techniques and cutting-edge deep learning methodologies, our framework achieves remarkable levels of accuracy. Experimental findings substantiate the effectiveness of our proposed approach in accurately identifying cataract and glaucoma, underscoring its potential for early detection and intervention in ocular diseases. [ABSTRACT FROM AUTHOR]