Research on an Intelligent Lightweight-Assisted Pterygium Diagnosis Model Based on Anterior Segment Images

Table 3

The five models’ evaluation results.

Model	Evaluation indicators	Normal	Observe	Surgery
MobileNet (original data)	Sensitivity	96.72%	72.58%	86.15%
	Specificity	96.06%	92.06%	89.43%
	F1-score	94.40%	76.92%	83.58%
	AUC	0.964	0.823	0.878
	95% CI	0.931-0.996	0.751-0.895	0.820-0.936
	Kappa	77.64%
	Accuracy	85.11%
	Size (MB)	13.5
	Parameters (million)	4.2
	Time-S (ms)	5.86
	Time-C (ms)	473.37
MobileNet (augmented data)	Sensitivity	96.72%	83.87%	84.62%
	Specificity	98.43%	90.48%	93.50%
	F1-score	96.72%	82.54%	85.94%
	AUC	0.976	0.872	0.891
	95% CI	0.947-1	0.811-0.933	0.833-0.948
	Kappa	82.44%
	Accuracy	88.30%
	Size (MB)	13.5
	Parameters (million)	4.2
	Time-S (ms)	5.75
	Time-C (ms)	465.53
AlexNet	Sensitivity	91.80%	83.87%	84.62%
	Specificity	98.43%	88.10%	77.61%
	F1-score	94.12%	80.62%	85.94%
	AUC	0.951	0.860	0.891
	95% CI	0.909-0.993	0.797-0.922	0.833-0.948
	Kappa	80.05%
	Accuracy	86.70%
	Size (MB)	233
	Parameters (million)	60
	Time-S (ms)	1.06
	Time-C (ms)	64.63
VGG16	Sensitivity	96.72%	79.03%	67.69%
	Specificity	92.13%	81.75%	97.56%
	F1-score	90.77%	73.13%	78.57%
	AUC	0.944	0.804	0.826
	95% CI	0.907-0.982	0.733-0.874	0.754-0.899
	Kappa	71.34%
	Accuracy	80.85%
	Size (MB)	527
	Parameters (million)	138
	Time-S (ms)	1.72
	Time-C (ms)	1020.11
ResNet18	Sensitivity	81.97%	66.13%	75.38%
	Specificity	95.28%	81.75%	84.55%
	F1-score	85.47%	65.08%	73.68%
	AUC	0.886	0.739	0.800
	95% CI	0.825-0.947	0.660-0.819	0.728-0.871
	Kappa	61.67%
	Accuracy	74.47%
	Size (MB)	44.6
	Parameters (million)	33
	Time-S (ms)	2.53
	Time-C (ms)	170.88