Neural networks are black-box classification models that can be trained, among other things, to classify the content of an image into different categories. This will be the use case addressed in this work. In some cases, neural networks surpass human precision. Therefore, the interest toward this technology has increased exponentially in the last few years. Although the performances are very high, the interpretability is low. In this thesis, we make contributions to the development of EBANO, an engine that generates transparency reports for black-box models. First, we compared EBANO's feature extraction step with image segmentation techniques, among those examined: flood fill, watershed, grabcuts and meanshift only watershed and meanshift were applicable to our case.