Szczegóły publikacji
Opis bibliograficzny
Particle swarm-optimized U-Net framework for precise multimodal brain tumor segmentation / Shoffan SAIFULLAH, Rafał DREŻEWSKI // W: GECCO'25 Companion [Dokument elektroniczny] : proceedings of the 2025 Genetic and Evolutionary Computation Conference Companion : July 14–18, 2025, Málaga, Spain. — Wersja do Windows. — Dane tekstowe. — New York : Association for Computing Machinery, 2025. — e-ISBN: 979-8-4007-1464-1. — S. 323–326. — Wymagania systemowe: Adobe Reader. — Bibliogr. s. 326, Abstr. — Publikacja dostępna online od: 2025-08-11. — S. Saifullah - dod. afiliacja: Universitas PembangunanNasional Veteran Yogyakarta, Indonesia
Autorzy (2)
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 161862 |
|---|---|
| Data dodania do BaDAP | 2025-09-03 |
| Tekst źródłowy | URL |
| DOI | 10.1145/3712255.3726561 |
| Rok publikacji | 2025 |
| Typ publikacji | materiały konferencyjne (aut.) |
| Otwarty dostęp |  |
| Creative Commons | |
| Wydawca | Association for Computing Machinery (ACM) |
| Konferencja | Genetic and Evolutionary Computations 2025 |
Abstract
Medical image segmentation for brain tumor analysis requires accurate and efficient models due to complex multimodal MRI data and tumor variability. This study presents PSO-UNet, which integrates Particle Swarm Optimization (PSO) with U-Net for dynamic hyperparameter tuning of filters, kernel size, and learning rate. PSOUNet achieves state-of-the-art segmentation with DSCs of 0.9578 and 0.9523 and IoU scores of 0.9194 and 0.9097 on BraTS 2021 and Figshare datasets. The model uses only 7.8M parameters and executes in 906 seconds, outperforming standard U-Net frameworks. PSO-UNet generalizes well across modalities and tumor types, offering a lightweight and clinically viable solution. This framework offers a novel integration of automated PSO-driven hyperparameter tuning into U-Net, enhancing segmentation performance while reducing computational overhead. Future work will explore hybrid optimizations and further scalability.
