Improving Retinopathy Classification Using Optimized Support Vector Machines and Deep Learning Techniques

Abstract

Retinopathy is one of the primary causes of blindness, making early detection and diagnosis challenging. Conventional techniques of retinopathy classification depend on manual observation and rule-driven methods, which may be time-consuming and error-prone. Machine learning algorithms, such as Support Vector Machines (SVMs) and Deep Learning (DL), have recently exhibited efficacy and substantial accuracy enhancements in classification. But problems like overfitting, parameters adjustment and the requirement for huge annotated datasets can limit the power of these models. Therefore, in this paper, we are proposing enhanced classification of retinopathy by-helping Support Vector Machines and deep learning model. More specifically, we present a hybrid approach which combines a deep convolutional neural network (CNN) for feature extraction with a finely-tuned SVM for classification. To improve the generalization ability of the SVM, we utilize a grid search approach to optimize the SVM parameters. We validate our method on a publicly available retinopathy dataset which shows a strong gain in classification accuracy over existing methods. We present our approach, with the results showing that, compared to traditional SVM and CNN-based models, we achieve an accuracy of 95%, while reducing false positives. Providing early detection and alleviating the burden on healthcare staff, this hybrid approach provides a more robust and scalable method for automated screening of retinopathy. Overall, we have presented a novel model that now automates retinopathy prediction to a point where it could be useful in the clinic.