Neural Network Ensemble for Detecting Parasite Eggs in Microscopic Images

Abstract

Intestinal parasite infections are a global health problem. In 2022, the WHO estimates that up to 1.2 billion people will be infected with Ascaris lumbricoides. Diagnosis is conducted by analyzing faecall samples under a microscope. However, this process is laborious and prone to error. Considering this, this study proposes a methodology to automate the detection of parasite eggs in microscope images. This methodology applies multiple object detectors in an ensemble and submits a model to reduce false negatives in the public dataset Chula-ParasiteEgg-11, with 11,000 images and 11 classes of parasites. Using this approach, it was possible to reduce the false negative rate and improve the f1 score up to 0.94. The results suggest that the proposed model leads to a reduction of false negatives and an improvement in recall.