# Fine-tuned Vision Language Model for Localization of Parasitic Eggs in Microscopic Images

arXiv: https://arxiv.org/abs/2602.13712 · HTML: https://mlsanity.com/paper/2602.13712/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 14 references that are in the map.
- First posted on arXiv 14 Feb 2026.

## Builds on (most cited first, 4 of 4)

- [Florence-2: Advancing a Unified Representation for a Variety of Vision Tasks](https://mlsanity.com/paper/2311.06242/) (2023)
- [A Robust Ensemble Model for Patasitic Egg Detection and Classification](https://mlsanity.com/paper/2207.01419/) (2022)
- [Localization and Classification of Parasitic Eggs in Microscopic Images Using an EfficientDet Detector](https://mlsanity.com/paper/2208.01963/) (2022)
- [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)

Soil-transmitted helminth (STH) infections continuously affect a large proportion of the global population, particularly in tropical and sub-tropical regions, where access to specialized diagnostic expertise is limited. Although manual microscopic diagnosis of parasitic eggs remains the diagnostic gold standard, the approach can be labour-intensive, time-consuming, and prone to human error. This paper aims to utilize a vision language model (VLM) such as Microsoft Florence that was fine-tuned to localize all parasitic eggs within microscopic images. The preliminary results show that our localization VLM performs comparatively better than the other object detection methods, such as EfficientDet, with an mIOU of 0.94. This finding demonstrates the potential of the proposed VLM to serve as a core component of an automated framework, offering a scalable engineering solution for intelligent parasitological diagnosis.