Table 6 The calculated metrics of CNN models according to ML algorithms.

Model	Method	Key metrics used to evaluate the performance of a model
Model	Method	Accuracy	Precision	Recall	F1 Score	MAE	RMSE	R2
VGG16	SVM	0.9460	0.9470	0.9461	0.9461	0.2769	1.3703	0.7693
	DT	0.7260	0.7260	0.7284	0.7281	1.1700	2.5883	0.1772
	KNN	0.8706	0.8806	0.8717	0.8723	0.5835	1.8555	0.5771
	RF	0.8930	0.8966	0.8951	0.8925	0.4490	1.5946	0.6877
MobileNetV2	SVM	0.9276	0.9275	0.9289	0.9278	0.3910	1.6240	0.6805
	DT	0.6853	0.7066	0.6902	0.6950	1.3737	2.7989	0.0510
	KNN	0.8146	0.8348	0.8196	0.8200	0.8910	2.3181	0.3448
	RF	0.8482	0.8469	0.8535	0.8471	0.7128	2.0611	0.4820
InceptionV3	SVM	0.8940	0.8946	0.8924	0.8914	0.5468	1.8697	0.5765
	DT	0.6720	0.6676	0.6685	0.6668	1.4745	2.9125	-0.0274
	KNN	0.7851	0.8152	0.7899	0.7834	1.0478	2.5120	0.2356
	RF	0.8136	0.8246	0.8133	0.8074	0.9327	2.4164	0.2927
Vgg19	SVM	0.9470	0.9486	0.9476	0.9477	0.25458	1.25796	0.7987
	DT	0.7331	0.7430	0.7337	0.7339	1.0906	2.4757	0.2203
	KNN	0.8920	0.9070	0.8972	0.8959	0.4287	1.5662	0.6879
	RF	0.9175	0.9177	0.9191	0.9172	0.3289	1.3360	0.7729
ResNet50	SVM	0.9551	0.9554	0.9537	0.9542	0.21384	1.1629	0.8374
	DT	0.7637	0.7636	0.7558	0.7574	1.0264	2.4281	0.2912
	KNN	0.9042	0.9147	0.9005	0.9048	0.5285	1.8865	0.5722
	RF	0.9063	0.9080	0.9030	0.9038	0.4714	1.7405	0.6358
DenseNet201	SVM	0.9215	0.9235	0.9263	0.9239	0.3604	1.4858	0.7321
	DT	0.7739	0.7763	0.7827	0.7785	0.9633	2.3179	0.3482
	KNN	0.8706	0.8857	0.8762	0.8779	0.5957	1.8930	0.5652
	RF	0.8808	0.8826	0.8868	0.8836	0.5142	1.6937	0.6520
Inceptionresnetv2	SVM	0.7250	0.7219	0.7217	0.7211	1.1578	2.5570	0.1897
	DT	0.5885	0.5970	0.5858	0.5876	1.6629	2.9869	-0.1055
	KNN	0.6649	0.6705	0.6579	0.6503	1.3584	2.6645	0.1069
	RF	0.7668	0.7642	0.7612	0.7598	0.9602	2.3049	0.3317

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