Table 2.

Classification accuracy (%) of our method and recent existing state-of-the-art methods

Methods	Accuracy (%)
DCNN+SVM, 2017 [1]	77.8
Pre-trained VGG-16, 2018 [2]	83.0
Ensemble of three DCNNs, 2018 [2]	87.0
Kwok et al., 2018 [19]	87.0
Makarchuk et al., 2018 [24]	90.0
EMS-Net, 2019 [38]	91.7
Our hybrid features + SVM	92.2
Our hybrid features + MLP	85.2
Our hybrid features + RF	80.2
Our hybrid features + XGBoost	82.7

Bold value indicates the best accuracy