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

2026-02-14

AI Summary

This study addresses the challenges of microscopic diagnosis of soil-transmitted helminth infections in resource-limited settings, where conventional methods are time-consuming, labor-intensive, and prone to human error. To overcome these limitations, the authors propose a scalable intelligent diagnostic system by fine-tuning Microsoft’s Florence vision-language model for parasite egg localization—a novel application in this domain. The approach significantly outperforms traditional object detection models such as EfficientDet, achieving a mean Intersection over Union (mIOU) of 0.94. These results demonstrate the effectiveness and substantial potential of vision-language models in automating parasitic disease diagnosis, offering a promising pathway toward more accurate and efficient diagnostic tools in low-resource environments.

Technology Category

• Computer Vision: Large Vision Models
• Natural Language Processing: Fact-Checking / Misinformation Detection (NLP Focus)
• Machine Learning: Hardware-aware ML

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines
• Web Mining and Content Analysis: Large pretrained models with web data

Abstract

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.

Problem

• parasitic egg localization
• microscopic image analysis
• soil-transmitted helminth
• automated diagnosis
• vision language model

Innovation

• Vision Language Model
• Fine-tuning
• Parasitic Egg Localization
• Microscopic Image Analysis
• Automated Diagnosis