Table 3.

Summary of the results showing the probability of error and detection, and the positive and negative predictive values based on the testing of training and non-training sets of images.

Classifier	Image set	$P_{\mathrm{eI}}$	$P_{\mathrm{eII}}$ (Miss)	Sensitivity	Specificity	PPV	NPV
GLM	Training calculated by image	14.39%	10.87%	89.13%	85.61%	40.20%	98.64%
	Non-training calculated by image	62.20%	38.46%	61.54%	37.80%	13.56%	86.11%
	Non-training calculated by ovary	75.76%	25.00%	75.00%	24.24%	10.71%	88.89%
NN	Training calculated by image	1.89%	13.04%	86.96%	98.11%	83.33%	98.58%
	Non-training calculated by image	37.80%	46.15%	53.85%	62.20%	18.42%	89.47%
	Non-training calculated by ovary	57.58%	25.00%	75.00%	42.42%	13.64%	93.33%
SVM	Training calculated by image	0.00%	0.00%	100.00%	100.00%	100.00%	100.00%
	Non-training calculated by image	4.88%	23.08%	76.92%	95.12%	71.43%	96.30%
	Non-training calculated by ovary	12.12%	0.00%	100.00%	87.88%	50.00%	100.00%

Note: $P_{\mathrm{eI}}$: False positive rate, $P_{\mathrm{eII}}$: False negative rate.