# Detection of Parasitic Eggs from Microscopy Images and the emergence of a new dataset

arXiv: https://arxiv.org/abs/2203.02940 · HTML: https://mlsanity.com/paper/2203.02940/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 16 references that are in the map.
- First posted on arXiv 6 Mar 2022.

## Builds on (most cited first, 8 of 8)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [Feature Pyramid Networks for Object Detection](https://mlsanity.com/paper/1612.03144/) (2016)
- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [Learning non-maximum suppression](https://mlsanity.com/paper/1705.02950/) (2017)
- [It GAN DO Better: GAN-based Detection of Objects on Images with Varying Quality](https://mlsanity.com/paper/1912.01707/) (2019)
- [Contextual colorization and denoising for low-light ultra high resolution sequences](https://mlsanity.com/paper/2101.01597/) (2021)
- [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, 1 of 1)

- [ICIP 2022 Challenge on Parasitic Egg Detection and Classification in Microscopic Images: Dataset, Methods and Results](https://mlsanity.com/paper/2208.06063/) (2022)

## Abstract (arXiv)

Automatic detection of parasitic eggs in microscopy images has the potential to increase the efficiency of human experts whilst also providing an objective assessment. The time saved by such a process would both help ensure a prompt treatment to patients, and off-load excessive work from experts' shoulders. Advances in deep learning inspired us to exploit successful architectures for detection, adapting them to tackle a different domain. We propose a framework that exploits two such state-of-the-art models. Specifically, we demonstrate results produced by both a Generative Adversarial Network (GAN) and Faster-RCNN, for image enhancement and object detection respectively, on microscopy images of varying quality. The use of these techniques yields encouraging results, though further improvements are still needed for certain egg types whose detection still proves challenging. As a result, a new dataset has been created and made publicly available, providing an even wider range of classes and variability.