Table 1.

The evaluation results of the KNN-based classification models.

Method	Training rate	Accuracy	Sensitivity	Specificity	Precision	Prevalence	Error Rate	False Pos.
LBP	50%	0.9830	0.9720	1.0000	1.0000	0.596	0.017	0.0000
	60%	0.9850	0.9750	1.0000	1.0000	0.594	0.015	0.0000
	70%	0.9880	0.9800	1.0000	1.0000	0.595	0.012	0.0000
	80%	0.9870	0.9780	1.0000	1.0000	0.597	0.013	0.0000
	90%	0.9900	0.9830	1.0000	1.0000	0.585	0.01	0.0000
HOG	50%	0.9350	0.9993	0.8677	0.8879	0.5129	0.065	0.1323
	60%	0.9380	0.9996	0.8724	0.8932	0.5163	0.062	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
Haralick	50%	0.9522	0.9378	0.9748	0.9825	0.6053	0.0478	0.0252
	60%	0.9539	0.9392	0.9769	0.9841	0.6067	0.0462	0.0231
	70%	0.9530	0.9364	0.9792	0.9858	0.6100	0.047	0.0208
	80%	0.9577	0.9417	0.9828	0.9880	0.6060	0.0423	0.0172
	90%	0.9588	0.9443	0.9806	0.9871	0.6011	0.0412	0.0194