Detection and classification of intestinal parasites using advanced object detection models
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 200...
| Main Authors: | , , |
|---|---|
| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
Atlantis Press
2025
|
| Subjects: | |
| Online Access: | https://umpir.ump.edu.my/id/eprint/45627/ |
| _version_ | 1848827469546651648 |
|---|---|
| author | Hamza, Haifa Kamarul Hawari, Ghazali Ahmad, Abubakar |
| author_facet | Hamza, Haifa Kamarul Hawari, Ghazali Ahmad, Abubakar |
| author_sort | Hamza, Haifa |
| building | UMP Institutional Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-15T04:01:13Z |
| format | Conference or Workshop Item |
| id | ump-45627 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T04:01:13Z |
| publishDate | 2025 |
| publisher | Atlantis Press |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-456272025-09-12T07:24:11Z https://umpir.ump.edu.my/id/eprint/45627/ Detection and classification of intestinal parasites using advanced object detection models Hamza, Haifa Kamarul Hawari, Ghazali Ahmad, Abubakar QA75 Electronic computers. Computer science T Technology (General) 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. Atlantis Press 2025 Conference or Workshop Item PeerReviewed pdf en https://umpir.ump.edu.my/id/eprint/45627/1/Detection%20and%20Classification%20of%20Intestinal%20Parasites%20Using%20Advanced%20Object%20Detection%20Models%20-%20haifa%20Ahmed.pdf Hamza, Haifa and Kamarul Hawari, Ghazali and Ahmad, Abubakar (2025) Detection and classification of intestinal parasites using advanced object detection models. In: Proceedings of the 3rd Lawang Sewu International Symposium on Engineering and Applied Sciences (LEWIS-EAS 2024). 3rd Lawang Sewu International Symposium on Engineering and Applied Sciences (LEWIS-EAS 2024) , 7 December 2024 , Semarang, Indonesia (Online). pp. 47-57.. ISSN 2352-5401 ISBN 978-94-6463-764-9 (Published) https://doi.org/10.2991/978-94-6463-764-9_6 |
| spellingShingle | QA75 Electronic computers. Computer science T Technology (General) Hamza, Haifa Kamarul Hawari, Ghazali Ahmad, Abubakar Detection and classification of intestinal parasites using advanced object detection models |
| title | Detection and classification of intestinal parasites using advanced object detection models |
| title_full | Detection and classification of intestinal parasites using advanced object detection models |
| title_fullStr | Detection and classification of intestinal parasites using advanced object detection models |
| title_full_unstemmed | Detection and classification of intestinal parasites using advanced object detection models |
| title_short | Detection and classification of intestinal parasites using advanced object detection models |
| title_sort | detection and classification of intestinal parasites using advanced object detection models |
| topic | QA75 Electronic computers. Computer science T Technology (General) |
| url | https://umpir.ump.edu.my/id/eprint/45627/ https://umpir.ump.edu.my/id/eprint/45627/ |