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All Journal Jurnal Teknik Informatika (JUTIF)
Supratiknyo, Supratiknyo
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Computer Science & IT

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Interpretable Hybrid YOLOv8s-GWO Framework for Bounding-Box Viral Pneumonia Detection on Kaggle Chest X-ray Images Jalaluddin Amron, Azmi; Paramita, Cinantya; Šolić, Petar; Supratiknyo, Supratiknyo
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 6 (2025): JUTIF Volume 6, Number 6, Desember 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.6.5419

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.
Co-Authors Jalaluddin Amron, Azmi Paramita, Cinantya Šolić, Petar