Fig 1.
General DR lesions observed in the fundus images.
Fig 2.
Stages of development of diabetic retinopathy.
Fig 3.
Proposed research methodology for DR diagnosis.
Fig 4.
(a) Retinal Lesions in Fundus Images, (b) hard exudates, (c) soft exudates, (d) haemorrhages, and (e) microaneurysms.
Fig 5.
GauGAN architecture.
Fig 6.
KCBGWO flowchart.
Fig 7.
FCEDN architecture.
Fig 8.
Overview of feature extraction with fusion approaches and the FuNet model.
Fig 9.
ELM configuration.
Table 1.
Mathematical representation of performance evaluation matrices.
Table 2.
Hyperparameters and performance metrics of BGWO with K-means clustering.
Table 3.
Optimization algorithms performance comparison across benchmark functions.
Fig 10.
Detailed portrayal of the diabetic retinopathy identification workflow.
(a) Initial retinal image, (b) Enhanced image post-preprocessing, (c) Identification of red lesions (RL), (d) Identification of bright lesions (BL), (e) Quantitative feature labels on bright lesions, and (f) Quantitative feature labels on red lesions.
Fig 11.
Feature extraction-based classification results using pre-trained CNN models.
Table 4.
Classification outcomes utilizing feature extraction from diverse pre-trained CNN models and various down sampling approaches.
Fig 12.
AUC for each stage.
Table 5.
IDRiD-based state-of-the-art comparison.