Detection and classification of intestinal parasites with bayesian-optimized model - Hamza, Haifa; Kamarul Hawari, Ghazali; Ahmad, Abubakar (The Science and Information (SAI) Organization Limited, 2025)
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.