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

TL;DR

This paper presents a fine-tuned vision language model that effectively localizes parasitic eggs in microscopic images, outperforming traditional object detection methods and aiding automated parasitological diagnosis.

Contribution

The study introduces a novel fine-tuned vision language model specifically for parasitic egg localization, demonstrating superior performance over existing object detection techniques.

Findings

VLM achieved an mIOU of 0.94 in localization tasks.

Outperformed EfficientDet in accuracy and efficiency.

Shows potential for automated parasitological diagnosis.

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.