Gastrintestinal parasites in humans and animals constitute a serious problem, which can cause physical and mental deficiencies, diseases, and death. Their diagnosis currently rely on the visual analysis of optical microscopy slides, being very susceptible to human errors. We have investigated the full automation of the diagnosis of gastrintestinal parasites in human and animals. The solution for the problem requires knowledge from Computer Science, Chemistry, Medicine, and Biology.

We aim at developing new parasitological techniques that can produce microscopy slides rich in parasites and almost free of fecal impurities, in order to make feasible their automated image acquisition and analysis. In this context, image processing and machine learning operators play a crucial role and the interactive machine learning techniques can be validated by the specialists in the application domain.