Table 4.

Comparing the results of pre-trained transfer learning models with the proposed ViT-GRU on the AdamW, Adam, and SGD optimizer.

Model	Optimizer	Precision (%)	Recall (%)	F1-score (%)	Training loss	Validation loss	Trainig time (s)	Training accuracy (%)	Test accuracy (%)
ResNet18	AdamW	91	91	90	0.0113	0.1577	5.83	99.88	94.29
	Adam	94	93	93	0.0124	0.1193	6.07	99.88	96.86
	SGD	65	81	72	1.4124	1.4792	5.63	81.25	80.57
ResNet50	AdamW	91	91	91	0.0435	0.2218	12.94	98.65	91.43
	Adam	92	92	92	0.0524	0.2454	13.03	98.65	92.86
	SGD	63	79	70	0.5341	0.5443	12.64	81.74	79.43
VGG16	AdamW	91	92	91	0.0588	0.1521	18.41	97.43	93.71
	Adam	93	93	93	0.0547	0.1415	18.9	99.02	94
	SGD	72	81	75	0.5593	0.5347	17.73	78.55	81.43
VGG19	AdamW	92	91	91	0.0540	0.1755	20.92	98.28	94.29
	Adam	91	91	91	0.0540	0.1755	20.92	98.9	94.29
	SGD	72	79	73	0.5951	0.5601	20.55	78.8	78.29
EfficientNet_V2_l	AdamW	91	91	91	0.0723	0.1646	17.36	96.56	92.65
	Adam	89	89	89	0.1131	0.2954	14.61	95.83	91.71
	SGD	68	76	72	6.0248	6.0263	14.57	50.86	51.14
MobileNet_V2	AdamW	85	86	85	0.2885	0.3368	6.43	90.2	85.14
	Adam	80	83	80	0.2819	0.3643	6.54	88.97	83.71
	SGD	74	84	77	3.6244	2.5056	11.82	82.27	84.86
DenseNet121	AdamW	97	97	97	0.0182	0.0892	13.87	99.88	97.71
	Adam	93	93	93	0.0311	0.1541	13.93	99.88	96.29
	SGD	72	83	73	2.7244	2.6056	13.82	80.27	82.86
AlexNet	AdamW	86	87	86	0.1983	0.2656	4.57	92.89	89.43
	Adam	89	89	88	0.1451	0.2399	4.05	96.08	91.14
	SGD	71	84	77	6.8541	6.8497	4.29	79.78	84
ViT-GRU	AdamW	97	97	97	0.0056	0.0374	47.93	99.93	98.97
	Adam	96	95	95	0.0089	0.1025	49.28	99.75	96.56
	SGD	66	81	73	0.4695	0.4606	48.3	81.02	81.66