# Precise detection of Eimeria oocysts in sheep: a deep learning model based on microscopic images

## TL;DR

This paper introduces YOLO-GA, a deep learning model that accurately detects Eimeria oocysts in sheep microscopy images, improving diagnostic efficiency and accuracy.

## Contribution

YOLO-GA integrates attention modules into YOLOv5 for enhanced detection of small, overlapping Eimeria oocysts in microscopy images.

## Key findings

- YOLO-GA achieved 98.9% mean average precision (mAP@0.5) on a dataset of 2000 microscopy images.
- The model outperformed recent detectors like YOLOv8 and DETR in precision and inference speed.
- YOLO-GA offers real-time performance and is suitable for scalable health monitoring in sheep flocks.

## Abstract

Parasitic infections remain a major cause of productivity loss in global livestock production. Traditional microscopic diagnostic methods are labor-intensive and require specialized veterinary expertise. Recent automated detection systems are hindered by limited annotated microscopy datasets and the difficulty of extracting discriminative features from small, overlapping targets.

We propose YOLO-GA, an enhanced object detection framework, for accurate identification of Eimeria oocysts in ovine microscopy images. Built upon the YOLOv5’s architecture, the model incorporates two lightweight attention modules: (1) Contextual Transformer (CoT) blocks for local–global contextual enhancement and (2) Normalized Attention Mechanisms (NAM) for adaptive feature recalibration. The proposed model is optimized for both accuracy and computational efficiency.

Experiments on a curated dataset of 2000 microscopy images (200× magnification) demonstrated that YOLO-GA achieves a mean (± standard deviation) average precision (mAP@0.5) of 98.9% ± 0.1, with 95.2% ± 0.3 precision and real-time inference speed. Comparative evaluations against recent detectors, including YOLOv8, YOLOv10 and DETR variants, confirmed the superior performance of YOLO-GA across multiple runs.

YOLO-GA offers a high-accuracy solution with balanced computational efficiency for automated detection of Eimeria oocysts under complex microscopy conditions. This work lays a foundation for intelligent diagnostics of ovine Eimeria coccidiosis and provides a reference for scalable health monitoring of sheep flocks, with potential extension to other small ruminant coccidiosis (e.g. goat Eimeria) pending further validation.

The online version contains supplementary material available at 10.1186/s13071-025-07092-4.