  Currently, approximately $4$ billion people are infected by intestinal
parasites worldwide. Diseases caused by such infections constitute a public
health problem in most tropical countries, leading to physical and mental
disorders, and even death to children and immunodeficient individuals. Although
subjected to high error rates, human visual inspection is still in charge of
the vast majority of clinical diagnoses. In the past years, some works
addressed intelligent computer-aided intestinal parasites classification, but
they usually suffer from misclassification due to similarities between
parasites and fecal impurities. In this paper, we introduce Deep Belief
Networks to the context of automatic intestinal parasites classification.
Experiments conducted over three datasets composed of eggs, larvae, and
protozoa provided promising results, even considering unbalanced classes and
also fecal impurities.
