Hybrid deep learning framework for diabetic retinopathy classification with optimized attention AlexNet

Diabetic retinopathy (DR) is a chronic condition associated with diabetes that can lead to vision impairment and, if not addressed, may progress to irreversible blindness. Consequently, it is essential to detect pathological changes in the retina to assess DR severity accurately. Manual examination of retinal disorders is often complex, time consuming, and susceptible to errors due to fine retinal disorder. In recent years, Deep Learning (DL) based optimizations have shown significant promises in improving DR recognition and classification. At last, the advanced classification method using metaheuristic optimization for grading severity in fundus images. This work presents an automated DR classification using metaheuristic optimization based advanced DL model. There are four stages are involved in the suggested DR classification. At first, the pre-processing stage is performed green channel conversion, CLAHE and Gaussian filtering (GF). Then, the fundus lesions are segmented by the Fuzzy Possibilistic C Ordered Means (FPCOM). Finally, the lesions are classified by Attention AlexNet based Improved Nutcracker Optimizer (At-AlexNet-ImNO). The ImNO optimizes the At-AlexNet's weights and hyperparameters and boosts the classification performance. The experimentation is performed on two benchmark datasets like APTOS-2019 Blindness-Detection and EyePacs. Accuracy, precision and recall values achieved are 99.23 %, 98 % and 98.2 % on APTOS-2019 and accuracy, precision and recall values achieved are 99.43 %, 98.2 % and 98.65 % on EyePacs respectively.