Table 2.

Summary of patch-level classification.

Trained without SMOTE
Method		Sensitivity	Specificity	Accuracy	AUC
Texture analysis	GLCM	68.54%	91.88%	84.87%	0.905
	Gabor	60.56%	91.01%	81.68%	0.871
	LBP	81.31%	88.74%	87.60%	0.927
	Fractal	76.99%	88.90%	85.93%	0.918
	GLRLM	80.98%	92.66%	89.85%	0.943
	First-order	58.71%	90.43%	80.43%	0.869
	All texture features	84.33%	92.56%	90.95%	0.955
Pre-trained networks	AlexNet	82.14%	88.78%	86.96%	0.957
	SqueezeNet	79.59%	91.21%	87.40%	0.968
	Inception-V3	87.58%	87.80%	88.92%	0.959
	ResNet-18	83.50%	92.56%	89.88%	0.963
	ResNet-50	93.13%	87.15%	89.96%	0.977
	InceptionResNet	89.57%	89.22%	90.50%	0.975
Trained with SMOTE
Method		Sensitivity	Specificity	Accuracy	AUC
Texture analysis	GLCM	84.96%	84.38%	85.51%	0.928
	Gabor	79.83%	82.50%	83.07%	0.894
	LBP	86.31%	83.87%	86.25%	0.926
	Fractal	88.76%	80.33%	84.39%	0.927
	GLRLM	84.95%	88.29%	88.43%	0.943
	First-order	82.27%	84.65%	84.98%	0.907
	All texture features	86.83%	88.86%	89.45%	0.95
Pre-trained networks	AlexNet	86.69%	77.70%	82.51%	0.948
	SqueezeNet	88.95%	90.10%	89.98%	0.957
	Inception-V3	91.73%	81.48%	86.14%	0.948
	ResNet-18	89.08%	85.43%	87.72%	0.952
	ResNet-50	90.83%	89.90%	90.87%	0.968
	InceptionResNet	88.95%	92.08%	91.71%	0.969