Table 6.

Performance of the proposed deep CNN and machine learning classifiers on brain dataset-1 and Figshare dataset with and without image registration.

Dataset	Preprocessing	Model	Sensitivity	Specificity	PPV	NPV	Accuracy	DSC
Brain dataset-1	Without image registration	Deep CNN + random forest	0.932	0.961	0.934	0.943	94.5%	92.4%
		Deep CNN + SVM-RBF	0.960	0.958	0.942	0.963	95.6%	93.3%
		Deep CNN + ELM	0.944	0.955	0.951	0.967	95.1%	94.6%
	With image registration	Deep CNN + random forest	0.951	0.982	0.964	0.969	97.2%	95.8%
		Deep CNN + SVM-RBF	0.983	0.986	0.973	0.992	98.3%	97.8%
		Deep CNN + ELM	0.965	0.984	0.972	0.982	98.0%	97.0%
Figshare dataset	Without image registration	Deep CNN + random forest	0.938	0.964	0.939	0.963	92.4%	91.8%
		Deep CNN + SVM-RBF	0.959	0.973	0.951	0.971	94.5%	93.2%
		Deep CNN + ELM	0.928	0.959	0.938	0.960	91.2%	90.4%
	With image registration	Deep CNN + random forest	0.956	0.979	0.957	0.977	97.8%	95.7%
		Deep CNN + SVM-RBF	0.971	0.984	0.967	0.985	98.0%	97.1%
		Deep CNN + ELM	0.947	0.975	0.950	0.973	96.7%	94.8%