Table 13 Precision, recall, and specificity comparison. Significant values are in bold.

Model	Precision (Before)	Precision (After)	Recall (Before)	Recall (After)	Specificity (Before)	Specificity (After)
ResNet-50⁴⁷	0.829	0.906	0.845	0.921	0.841	0.913
DenseNet-121⁴⁸	0.847	0.914	0.857	0.927	0.859	0.921
EfficientNet-B3⁴⁹	0.853	0.925	0.865	0.937	0.859	0.932
Vision Transformer⁵⁰	0.842	0.931	0.855	0.940	0.847	0.937
MobileNetV3⁵¹	0.815	0.883	0.827	0.896	0.827	0.890
Inception-v4⁵²	0.838	0.910	0.849	0.924	0.849	0.918
Swin Transformer⁵³	0.856	0.934	0.867	0.945	0.861	0.939
ConvNeXt⁵⁴	0.848	0.920	0.859	0.934	0.854	0.927
RegNet-Y⁵⁵	0.828	0.897	0.843	0.912	0.835	0.904
NFNet⁵⁶	0.859	0.938	0.870	0.951	0.865	0.942
Average	0.842	0.916	0.854	0.929	0.850	0.922

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