TABLE 1.

Color feature equations.

Color Features	Equations
Absolute RGB	Absolute R = Red Average over mask
	Absolute G = Green Average over maskE*
	Absolute B = Blue Average over maskE*, #
Relative RGB	Relative R = Absolute R - Skin R
	Relative G = Absolute G - Skin GE*
	Relative B = Absolute G - Skin B
RGB Chromaticity	R Chromaticity = R/(R+G+B)#
	G Chromaticity = G/(R+G+B)
	B Chromaticity = B/(R+G+B)
Relative RGB Chromaticity	Relative R Chromaticity = Relative R/Relative(R+G+B)
	Relative G Chromaticity = Relative G/Relative(R+G+B)#
	Relative B Chromaticity = Relative B/Relative(R+G+B)
RGB Variance	R Variance = Σ(R- total_R)2
	G Variance = Σ(G- total_G)2
	B Variance = Σ(B- total_B)2 E*, #
Relative Color Ratios	Relative B/Relative R
	Relative B/Relative G
	Relative G/Relative R
Converting RGB Color Plane to XYZ Color Plane	R to X = (R*0.49 + G*0.31 + B*0.20)/0.17697E*
	G to Y = (R*0.17697 + G*0.81240 + B*0.01063)/0.17697
	B to Z = (R*0.0 + G*0.01 + B*0.99)/0.17697#

Features in the final logistic regression model for BCC identification are marked by an emboldened “E*”, indicating their retention within the best-fit ellipse circumscribing the B-GO.

“#” indicates the final model of the expanded B-GO mask