| 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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