# Localization and Classification of Parasitic Eggs in Microscopic Images Using an EfficientDet Detector

arXiv: https://arxiv.org/abs/2208.01963 · HTML: https://mlsanity.com/paper/2208.01963/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 27 references that are in the map.
- First posted on arXiv 3 Aug 2022.

## Builds on (most cited first, 3 of 3)

- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [EfficientDet: Scalable and Efficient Object Detection](https://mlsanity.com/paper/1911.09070/) (2019)
- [Parasitic Egg Detection and Classification in Low-cost Microscopic Images using Transfer Learning](https://mlsanity.com/paper/2107.00968/) (2021)

## Built on by (most cited first, 2 of 2)

- [ICIP 2022 Challenge on Parasitic Egg Detection and Classification in Microscopic Images: Dataset, Methods and Results](https://mlsanity.com/paper/2208.06063/) (2022)
- [Fine-tuned Vision Language Model for Localization of Parasitic Eggs in Microscopic Images](https://mlsanity.com/paper/2602.13712/) (2026)

## Abstract (arXiv)

IPIs caused by protozoan and helminth parasites are among the most common infections in humans in LMICs. They are regarded as a severe public health concern, as they cause a wide array of potentially detrimental health conditions. Researchers have been developing pattern recognition techniques for the automatic identification of parasite eggs in microscopic images. Existing solutions still need improvements to reduce diagnostic errors and generate fast, efficient, and accurate results. Our paper addresses this and proposes a multi-modal learning detector to localize parasitic eggs and categorize them into 11 categories. The experiments were conducted on the novel Chula-ParasiteEgg-11 dataset that was used to train both EfficientDet model with EfficientNet-v2 backbone and EfficientNet-B7+SVM. The dataset has 11,000 microscopic training images from 11 categories. Our results show robust performance with an accuracy of 92%, and an F1 score of 93%. Additionally, the IOU distribution illustrates the high localization capability of the detector.