Szczegóły publikacji
Opis bibliograficzny
A hybrid particle swarm–genetic algorithm framework for U-Net hyperparameter optimization in high-precision brain tumor MRI segmentation / Shoffan SAIFULLAH, Rafał DREŻEWSKI, Anton Yudhana, Radius Tanone, Andiko Putro Suryotomo // Applied Sciences (Basel) [Dokument elektroniczny]. — Czasopismo elektroniczne ; ISSN 2076-3417 . — 2026 — vol. 16 iss. 6 art. no. 3041, s. 1–46. — Wymagania systemowe: Adobe Reader. — Bibliogr. s. 42–46, Abstr. — Publikacja dostępna online od: 2026-03-21. — S. Saifullah - dod. afiliacja: Universitas Pembangunan Nasional Veteran Yogyakarta, Yogyakarta, Indonesia. — R. Dreżewski - dod. afiliacja: Universitas Ahmad Dahlan, Yogyakarta, Indonesia
Autorzy (5)
- AGHSaifullah Shoffan
- AGHDreżewski Rafał
- Yudhana Anton
- Tanone Radius
- Suryotomo Andiko Putro
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 166831 |
|---|---|
| Data dodania do BaDAP | 2026-03-30 |
| Tekst źródłowy | URL |
| DOI | 10.3390/app16063041 |
| Rok publikacji | 2026 |
| Typ publikacji | artykuł w czasopiśmie |
| Otwarty dostęp |  |
| Creative Commons | |
| Czasopismo/seria | Applied Sciences (Basel) |
Abstract
Accurate and robust brain tumor segmentation remains a critical challenge in medical image analysis due to high inter-patient variability, complex tumor morphology, and modality-specific noise in MRI scans. This study proposes PSO-GA-U-Net, a novel hybrid deep learning framework that integrates Particle Swarm Optimization (PSO) and Genetic Algorithms (GAs) to optimize the U-Net architecture, enhancing segmentation performance and generalization. PSO dynamically tunes the learning rate to accommodate modality-specific variations, while the GA adaptively regulates dropout to improve feature diversity and reduce overfitting. The model was evaluated on three benchmark datasets—FBTS, BraTS 2021, and BraTS 2018—using five-fold cross-validation. PSO-GA-U-Net achieves Dice Similarity Coefficients (DSC) of 0.9587, 0.9406, and 0.9480 and Jaccard Index (JI) scores of 0.9209, 0.8881, and 0.9024, respectively, consistently outperforming state-of-the-art models in both overlap accuracy and boundary delineation. Statistical tests confirm that these improvements are significant across folds (𝑝 < 0.05). Visual heatmaps further illustrate the model’s ability to preserve structural integrity across tumor types and modalities. These results indicate that metaheuristic-guided deep learning offers a promising and clinically applicable solution for automatic tumor segmentation in radiological workflows.
