Table 1.

Classification results from the basic models and the hybrid model structures.

		Loss	Accuracy	Sensitivity	Specificity	Precision	F1 score
Basic models	EfficientNetB4	CE	0.8158	0.9442	0.7182	0.9027	0.9230
		E-CE	0.8544	0.9736	0.7061	0.9017	0.9362
	EfficientNetB5	CE	0.7932	0.9254	0.6782	0.8884	0.9065
		E-CE	0.8488	0.9809	0.6549	0.8872	0.9317
	NASNetLarge	CE	0.7828	0.9151	0.7031	0.8951	0.9050
		E-CE	0.8470	0.9845	0.6353	0.8820	0.9304
	InceptionResNetV2	CE	0.7888	0.9657	0.5177	0.8471	0.9025
		E-CE	0.8502	0.9739	0.6963	0.8987	0.9348
	Xception	CE	0.8100	0.9706	0.5742	0.8632	0.9138
		E-CE	0.8476	0.9848	0.6217	0.8781	0.9284
Hybrid model	Hybrid-model-a	CE	0.8584	0.9877	0.6481	0.8860	0.9341
	Hybrid-model-f	CE	0.8626	0.9652	0.7476	0.9137	0.9387
	Hybrid-model-c	CE	0.8634	0.9706	0.7325	0.9094	0.9390

CE indicates cross-entropy loss function; E-CE indicates enhance cross-entropy loss function; and the bold values indicate the best results.