Intestinal Parasites Classification Using Deep Belief Networks

TL;DR

This paper introduces a deep learning approach using Deep Belief Networks for automatic classification of intestinal parasites, aiming to improve accuracy over traditional visual inspection methods.

Contribution

The study applies Deep Belief Networks to classify intestinal parasites, addressing challenges like class imbalance and fecal impurities with promising experimental results.

Findings

Effective classification of eggs, larvae, and protozoa.

Robust performance despite unbalanced datasets.

Improved accuracy over existing methods.

Abstract

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.