Table 3. Classification accuracy obtained using handcrafted and radiomic features.

Features	LDA	kNN	GNB	SVM	AdaBoost	RF	Ensemble	XGBoost	NN
SIFT	0.656	0.720	0.608	0.615	0.665	0.725	0.739	0.720	0.780
GIST	0.674	0.730	0.688	0.605	0.690	0.705	0.756	0.730	0.764
LBP	0.689	0.658	0.660	0.626	0.690	0.710	0.748	0.716	0.791
HOG	0.686	0.658	0.660	0.626	0.690	0.710	0.746	0.711	0.790
GLCM	0.699	0.722	0.679	0.657	0.724	0.751	0.769	0.738	0.820
Radiomics	0.769	0.838	0.745	0.727	0.764	0.830	0.849	0.841	0.876