An effectual framework for retinal disease classification using KELM optimized by improved squirrel search algorithm

The rapid increase in screen time across all age groups worldwide has led to simultaneous surge in retinal diseases too. This article aims to shed light on the different kinds of retinal disease classification such as Age-related Macular Degeneration (AMD), glaucoma, cataract, hypertensive retinopathy, pathological myopia etc. The proposed work avails 8000 retinal fundus images from Ocular Disease Intelligent Recognition dataset applying unsharp mask filter for preprocessing and SegNet algorithm for segmentation. YOLOv11 technique is used for extracting features and Kernel Extreme Learning Machine for classification. The results are finally optimized using Improved Squirrel Search Optimization algorithm for betterment. Comparative analysis is done against existing classifiers of ResNet50, DenseNet201 and EfficientNet. Results show that the proposed system has achieved a hold-out test accuracy of 98.3%, recall score of 97.5%, precision value of 96.9%, F1-score of 97.1% and MCC value of 96.4%. A 5-fold cross-validation further confirmed generalizability, yielding an average accuracy of 97.63%, which is considered the main estimate of model generalization.