Table 4.

Related works in diabetic retinopathy diagnosis part - 3.

S. no.	References	Methodologies	Data source	Advantages	Disadvantages and gaps
21	Bilal et al.29	AI-based automatic detection and classification using U-Net and deep learning	Retinal fundus images	Highly effective in image segmentation for disease classification	Limited to dataset characteristics, lacking generalizability across diverse populations
22	Bilal et al.30	Mixed models for disease grading and severity classification	Diabetic Retinopathy Grading Database	Improved adaptability of ensemble models for complex grading	Mixed models may increase computational complexity
23	Bilal et al.31	Transfer learning with U-Net for enhanced detection accuracy	Retinal fundus images	Efficient feature extraction with pretrained models	Transfer learning limited by domain specificity
24	Bilal et al.32	Grey Wolf Optimization with CNN for feature selection	Retinal images	Enhanced feature selection and classification accuracy	Optimization technique may not generalize well to all data types
25	Bilal et al.33	CNNs with weighted filters and adaptive filtering for classification	Retinal fundus images	Effective noise reduction and improved classification accuracy	Increased model complexity and training time
26	Bilal et al.34	CNN-SVD-enhanced SVM for detecting vision-threatening retinopathy	Retinal fundus images	Robust detection capabilities through hybrid model	Complex hybrid structure may require significant computational resources
27	Bilal et al.35	EdgeSVDNet, 5G-enabled for real-time diagnosis	Retinal fundus images with 5G connectivity	Enhanced accessibility and speed for remote diagnostics	Dependent on 5G infrastructure, limited in areas without high-speed connectivity
28	Bilal et al.36	NIMEQ-SACNet model with self-attention for precision medicine	Retinal image data for precision diagnostics	High accuracy and adaptability for precision medicine applications	Complexity of self-attention mechanism may increase model size and training requirements