Detection and Classification of Intestinal Parasites Using Advanced Object Detection Models

DOI

10.2991/978-94-6463-764-9_6How to use a DOI?

Keywords

Intestinal Parasites; Faster RCNN; YOLOv8; Machine Learning; Deep Learning; Object Detection

Abstract

Automated detection of intestinal parasites is crucial for improving diagnostic efficiency and accuracy in parasitology. This study evaluates the performance of three object detection models: Faster RCNN with ResNet back-bone, Faster RCNN with RetinaNet backbone, and YOLOv8. A dataset comprising 2000 microscopic images of two parasite species, Ascaris lumbricoides and Trichuris trichiura, was used. The dataset was split into 1500 images for training, 300 for validation, and 200 for testing. Experimental results show that Faster RCNN with RetinaNet achieved the highest Average Precision (AP) across varying Intersection over Union (IoU) thresholds, demonstrating its robustness. YOLOv8 exhibited superior precision at low confidence thresholds, while Faster RCNN with ResNet demonstrated strong recall consistency. These findings provide a comparative analysis, highlighting the strengths and limitations of each model for reliable and efficient intestinal parasite detection, suggesting the integration of ensemble models to combine RetinaNet’s robustness and YOLOv8’s precision-recall capabilities for optimized results.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Haifa Hamza
AU  - Kamarul Hawari Ghazali
AU  - Abubakar Ahmad
PY  - 2025
DA  - 2025/07/30
TI  - Detection and Classification of Intestinal Parasites Using Advanced Object Detection Models
BT  - Proceedings of the 3rd Lawang Sewu International Symposium on Engineering and Applied Sciences (LEWIS-EAS 2024)
PB  - Atlantis Press
SP  - 47
EP  - 57
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6463-764-9_6
DO  - 10.2991/978-94-6463-764-9_6
ID  - Hamza2025
ER  -