A computer aided detection (CAD) system suffers from vagueness and imprecision in both medical science and image processing techniques. These uncertainty issues in the classification components of a CAD system directly influence the accuracy. This paper takes advantage of type-2 fuzzy sets as three-dimensional fuzzy sets with high potential for managing uncertainty issues in vague environments. In this paper, an automatic optimized approach for generating and tuning type-2 Gaussian membership function (MF) parameters and their footprint of uncertainty is proposed. In this approach, two interval type-2 fuzzy logic system (IT2FLS) methods based on the Mamdani rules model are presented for tackling the uncertainty issues in classification problems in pattern recognition. Furthermore, the Genetic algorithm is employed for tuning of the MFs parameters and footprint of uncertainty. In order to assess the performance, the designed IT2FLSs are applied on a lung CAD application for classification of nodules. The ROC accuracy and mean absolute error (MAE) are considered as the performance indicators. The results reveal that the Genetic IT2FLS classifier outperforms the equivalent type-1 FLS and is capable of capturing more uncertainties.