ICIP 2022 Challenge on Parasitic Egg Detection and Classification in Microscopic Images: Dataset, Methods and Results

TL;DR

This paper reviews the ICIP 2022 Challenge focused on automated detection and classification of parasitic eggs in microscopic images, introducing a large dataset and summarizing participant methods and results.

Contribution

It presents the largest dataset for parasitic egg detection, details the challenge setup, and analyzes various methods used by participants.

Findings

Largest dataset of its kind for parasitic egg detection

Summary of diverse methods and their performance

Insights into effective approaches for automated classification

Abstract

Manual examination of faecal smear samples to identify the existence of parasitic eggs is very time-consuming and can only be done by specialists. Therefore, an automated system is required to tackle this problem since it can relate to serious intestinal parasitic infections. This paper reviews the ICIP 2022 Challenge on parasitic egg detection and classification in microscopic images. We describe a new dataset for this application, which is the largest dataset of its kind. The methods used by participants in the challenge are summarised and discussed along with their results.