Table 3.

Comparison of the best classification performance (mean ± std) achieved with feature reduction techniques - PCA, XOM, t-SNE and MI, as well as the corresponding dimensions of the output feature vectors. The classification performance achieved without feature reduction (none) is also included. As seen here, the best results are obtained for area and perimeter when used with Sammon’s mapping or XOM (in conjunction with RBFN-FA); XOM achieves this with the smallest lower-dimensional representation. All results for PCA, XOM and t-SNE were obtained with the CADx methodology proposed in this study.

Feature	Technique	AUC	Dimension
Area	PCA	0.81 ± 0.09	20
	Sammon	0.82 ± 0.08	20
	XOM	0.83 ± 0.10	2
	t-SNE	0.76 ± 0.11	20
	MI	0.81 ± 0.10	50
	none	0.83 ± 0.09	100
Perimeter	PCA	0.80 ± 0.10	20
	Sammon	0.84 ± 0.10	20
	XOM	0.82 ± 0.09	2
	t-SNE	0.74 ± 0.12	5
	MI	0.80 ± 0.09	30
	none	0.82 ± 0.09	100
Euler char.	PCA	0.72 ± 0.09	3
	Sammon	0.70 ± 0.08	3
	XOM	0.71 ± 0.11	5
	t-SNE	0.71 ± 0.09	5
	MI	0.69 ± 0.09	20
	none	0.68 ± 0.09	100