Table 2.

The evaluation results of the SVM-based classification models

Method	Training rate	Accuracy	Sensitivity	Specificity	Precision	Prevalence	Error Rate	False Pos.
LBP	50%	0.9350	0.9993	0.8677	0.8879	0.5129	0.0650	0.1323
	60%	0.9380	0.9996	0.8724	0.8932	0.5163	0.0620	0.1276
	70%	0.9447	0.9992	0.8850	0.9053	0.5239	0.0553	0.1150
	80%	0.9454	0.9995	0.8844	0.9066	0.5293	0.0547	0.1156
	90%	0.9498	0.9998	0.8956	0.9122	0.5200	0.0502	0.1044
HOG	50%	0.8513	0.9536	0.8086	0.8189	0.5176	0.1487	0.1914
	60%	0.8857	0.9803	0.8204	0.8330	0.4979	0.1143	0.1796
	70%	0.9011	0.9968	0.8163	0.8342	0.4863	0.0989	0.1837
	80%	0.9074	0.9999	0.8200	0.8402	0.4881	0.0926	0.1800
	90%	0.9147	0.9996	0.8324	0.8529	0.4935	0.0853	0.1676
Haralick	50%	0.9364	0.9995	0.8684	0.8914	0.5205	0.0636	0.1316
	60%	0.9436	0.9995	0.8819	0.9033	0.5250	0.0565	0.1181
	70%	0.9515	0.9994	0.8978	0.9167	0.5309	0.0485	0.1022
	80%	0.9538	0.9998	0.9024	0.9199	0.5291	0.0463	0.0976
	90%	0.9586	0.9997	0.9112	0.9288	0.5382	0.0414	0.0888