Table 1.

(a) Characteristics of the datasets used in the proposed approach and (b) Performance metrics used to assess the proposed approach. True Positive (TP): Number of samples correctly classified as positive, False Positive (FP): Number of samples incorrectly classified as positive, True Negative (TN): Number of samples correctly classified as negative, False Negative (FN): Number of samples incorrectly classified as negative, Balanced Accuracy (BACC): A measure of average accuracy across all classes, giving equal importance to each class, Positive Predictive Value (PPV) represents the proportion of true positive predictions (correctly predicted positive instances) among all instances predicted as positive, Negative Predictive Value(NPV) represents the proportion of true negative predictions (correctly predicted negative instances) among all instances predicted as negative, Kappa Statistic (K) quantifies the agreement between classifier and true labels, adjusting for chance agreement. Mean Intersection over Union (mIoU) measures the overlap between predicted mask (A) and ground truth mask (B) and Mean Dice Coefficient (mDC) measures the similarity between predicted mask (A) and Ground truth mask (B).

Dataset	Origin	H	G	DR	Vascular Mask	Optic Disc Mask
PAPILA40	Spain	333	155	0	No	Yes
HRF41	-	15	15	15	Yes	No
SUSTech42	China	631	0	588	No	Yes
FIVES43	China	200	200	200	Yes	No
Drishti-GS144,45	India	50	51	0	No	Yes

Metric	Formula
Precision	TP / (TP + FP)
Recall	TP / (TP + FN)
F1 Score	2 x (Precision x Recall) / (Precision + Recall)
Sensitivity	TP / (TP + FN)
Specificity	TN / (TN + FN)
Classwise Accuracy (CA)	TP / (TP + FP)
Balanced Accuracy (BACC)	(TP1 + TP2 +... + TPn) / N
Positive Predictive Value (PPV)	TP / (TP + FP)
Negative Predictive Value (NPV)	TN / (TN + FN)
Kappa Statistic (K)
Mean Intersection over Union (mIoU)
Mean Dice Coefficient (mDC)