Interpretable Hybrid YOLOv8s-GWO Framework for Bounding-Box Viral Pneumonia Detection on Kaggle Chest X-ray Images

Cinantya  Paramita; Azmi Jalaluddin Amron; Petar  Šolić; Supratiknyo Supratiknyo

doi:10.52436/1.jutif.2025.6.6.5419

Authors

Cinantya Paramita Dinus Research Group for AI in Medical Science (DREAMS), Faculty of Computer Science, Universitas Dian Nuswantoro, Semarang, Indonesia
Azmi Jalaluddin Amron Faculty of Computer Science, Universitas Dian Nuswantoro, Semarang, Indonesia
Petar Šolić Faculty of Electrical Engineering, Mechanical Engineering and Naval Architecture, University of Split, Split, Croatia
Supratiknyo SMK Sunan Kalijaga Demak, Demak, Indonesia

DOI:

https://doi.org/10.52436/1.jutif.2025.6.6.5419

Keywords:

Grey Wolf Optimizer, Hyperparameter Optimization, Medical Image Detection, Viral Pneumonia, YOLOv8s, Chest X-ray

Abstract

Viral pneumonia continues to impose a substantial global health burden, making rapid and reliable radiographic detection essential for early clinical management. This study proposes a hybrid framework integrating the YOLOv8s detection model with the Grey Wolf Optimizer (GWO) to enhance hyperparameter tuning for Viral Pneumonia identification in chest X-ray images. A curated set of Normal and Viral Pneumonia samples was manually annotated and preprocessed before training. The optimization process involved multi-stage refinement of learning rate, momentum, weight decay, and loss-gain parameters to improve convergence stability and detection accuracy. The optimized YOLOv8s + GWO model demonstrated notable performance gains, achieving 0.965 recall, 0.983 mAP@50, and 0.827 mAP@50–95 on internal evaluations. External testing further validated its robustness, delivering 98.80% accuracy, 99.48% specificity, and 97.46% sensitivity. These results highlight not only enhanced clinical diagnostic reliability but also contributions to Informatics and Computer Science, demonstrating the effectiveness of metaheuristic-guided optimization in improving deep-learning model performance, generalization, and computational efficiency for AI-driven image detection tasks.

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