Intestinal Parasites Classification Using Deep Belief Networks
This work addresses a public health issue in tropical countries by improving diagnostic accuracy for intestinal parasite infections, though it is incremental as it applies an existing deep learning method to a specific domain.
The paper tackled the problem of classifying intestinal parasites from microscopic images, which is prone to human error, by introducing Deep Belief Networks, achieving promising results on three datasets with unbalanced classes and fecal impurities.
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.