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Figure 7 Overall network performance for uniform parameter settings.

It is also worth mentioning that, if a multi-variate exhaustive search approach is used, the addition of a new cell to an existing (i.e., already planned) scenario needs complete re-simulation of the whole scenario to build the new curve for uniform parameter settings.

The analysis is first focused on network performance obtained with uniform parameter settings.

All numbers are for best parameter settings.

We used 10 SCORM samples for optimizing parameter settings for each method and then evaluated method performance at optimal parameters using 100 SCORM samples.

Table 4 shows obtained biometric performance after applying the proposed transform for different parameter settings.

For typical parameter settings in our simulations tumor cells move at about 0.1 pixels/MCS.

(See Additional file 1 for the parameter settings).

Default priors were used for parameter settings.

We evaluate our approach using different feature sets and parameter settings for the employed classifiers.

After that, we present experimental results achieved for various classifiers and parameter settings.