Table 2.

Accuracy, Precision, F1-Score, and Recall obtained through the classification of extracted features with classical extractors.

Extractors	Classifiers	Accuracy	Precision	F1 Score	Recall
GLCM	Naive Bayes	64.44 ± 1.22	81.61 ± 0.40	46.55 ± 2.96	54.29 ± 1.56
	MLP	64.44 ± 1.22	81.61 ± 0.40	46.55 ± 2.96	54.29 ± 1.56
	kNN	86.11 ± 3.23	87.70 ± 3.97	84.66 ± 3.69	83.55 ± 3.73
	RF	87.22 ± 2.87	89.06 ± 3.40	85.86 ± 3.18	84.56 ± 3.12
	SVM Linear	73.22 ± 3.12	78.87 ± 4.39	66.47 ± 4.96	66.71 ± 4.05
	SVM Polynomial	64.44 ± 1.22	81.61 ± 0.40	46.55 ± 2.96	54.29 ± 1.56
	SVM RBF	72.22 ± 2.77	84.11 ± 1.14	62.83 ± 4.91	64.34 ± 3.62
HU	Naive Bayes	61.11 ± 0.00	30.56 ± 0.00	37.93 ± 0.00	50.00 ± 0.00
	MLP	61.11 ± 0.00	30.56 ± 0.00	37.93 ± 0.00	50.00 ± 0.00
	kNN	80.44 ± 3.19	81.64 ± 4.05	78.14 ± 3.85	77.19 ± 3.74
	RF	80.67 ± 3.30	81.59 ± 4.08	78.51 ± 3.82	77.58 ± 3.78
	SVM Linear	52.89 ± 3.79	55.34 ± 3.52	52.81 ± 3.78	55.38 ± 3.64
	SVM Polynomial	51.89 ± 2.77	55.72 ± 2.91	51.75 ± 2.87	55.44 ± 2.84
	SVM RBF	50.56 ± 4.28	57.97 ± 3.80	49.31 ± 5.34	56.17 ± 3.54
LBP	Naive Bayes	61.11 ± 0.00	30.56 ± 0.00	37.93 ± 0.00	50.00 ± 0.00
	MLP	61.11 ± 0.00	30.56 ± 0.00	37.93 ± 0.00	50.00 ± 0.00
	kNN	83.89 ± 2.87	84.81 ± 4.13	82.41 ± 2.89	81.47 ± 2.64
	RF	87.33 ± 3.82	89.08 ± 3.99	85.96 ± 4.35	84.75 ± 4.49
	SVM Linear	66.89 ± 3.33	65.00 ± 3.94	63.21 ± 3.81	63.09 ± 3.55
	SVM Polynomial	38.89 ± 0.00	19.44 ± 0.00	28.00 ± 0.00	50.00 ± 0.00
	SVM RBF	68.56 ± 2.33	71.01 ± 5.78	60.57 ± 3.66	61.70 ± 2.77

The bold values are mean and standard deviation, respectively.