Detection and classification of intestinal parasites with bayesian-optimized model
Automated detection of intestinal parasites in medical imaging enhances diagnostic efficiency and reduces human error. This study evaluates object detection techniques using Faster R-CNN with different backbone architectures such as ResNet, RetinaNet, ResNext and YOLOv8 series for detecting Ascaris...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | en |
| Published: |
The Science and Information (SAI) Organization Limited
2025
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| Subjects: | |
| Online Access: | http://umpir.ump.edu.my/id/eprint/45625/1/Detection_and_Classification_of_Intestinal_Parasites%20%281%29%20-%20haifa%20Ahmed.pdf http://umpir.ump.edu.my/id/eprint/45625/ https://dx.doi.org/10.14569/IJACSA.2025.0160492 |
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| Summary: | Automated detection of intestinal parasites in medical imaging enhances diagnostic efficiency and reduces human error. This study evaluates object detection techniques using Faster R-CNN with different backbone architectures such as ResNet, RetinaNet, ResNext and YOLOv8 series for detecting Ascaris lumbricoides and Trichuris trichiura in microscopic images. A dataset of 2000 images was split into training (1500), validation (300), and testing (200). Results show Faster R-CNN with RetinaNet achieves the highest Average Precision (AP) across varying Intersection over Union (IoU) thresholds, making it robust in feature extraction. However, YOLOv8 excels in real-time detection, with YOLOv8n (nano) providing the best trade-off between accuracy and computational efficiency. Bayesian Optimization further improves YOLOv8n, achieving an AP of 99.6% and an Average Recall (AR) of 99.7%, surpassing two-stage architectures. This study highlights the potential of deep learning for automated parasite detection, reducing reliance on manual microscopy. Future research should explore transformer-based models, self-supervised learning, and mobile deployment for real-world clinical applications. |
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