Szczegóły publikacji
Opis bibliograficzny
Bayesian Optimization-driven U-Net architecture tuning for brain tumor segmentation / Shoffan SAIFULLAH, Rafał DREŻEWSKI // Engineering Proceedings [Dokument elektroniczny]. — Czasopismo elektroniczne ; ISSN 2673-4591 . — 2026 — vol. 124 iss. 1 art. no. 22, s. 1–12. — Wymagania systemowe: Adobe Reader. — Bibliogr. s. 11–12, Abstr. — Publikacja dostępna online od: 2026-02-09. — S. Saifullah - dod. afiliacja: Department of Informatics, Universitas Pembangunan Nasional Veteran Yogyakarta, Indonesia. — 6th International Electronic Conference on Applied Sciences, 9–11 December 2025
Autorzy (2)
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 166053 |
|---|---|
| Data dodania do BaDAP | 2026-02-25 |
| Tekst źródłowy | URL |
| DOI | 10.3390/engproc2026124022 |
| Rok publikacji | 2026 |
| Typ publikacji | referat w czasopiśmie |
| Otwarty dostęp |  |
| Creative Commons | |
| Czasopismo/seria | Engineering Proceedings |
Abstract
Precise brain tumor segmentation from magnetic resonance imaging (MRI) scans is critical for clinical diagnosis and treatment planning. However, determining an optimal deep learning architecture for such tasks remains a challenge due to the vast hyperparameter space and structural variations. This paper presents a novel approach that integrates Bayesian Optimization (BO) to automatically tune the U-Net architecture for effective brain tumor segmentation. The proposed BO-UNet framework searches over encoder, bottleneck, and decoder configurations using a Gaussian Process-based surrogate model, guided by a fitness function derived from Dice Similarity Coefficient (DSC) and Jaccard Index (JI). Experiments were conducted on two benchmark datasets: the Figshare Brain Tumor Segmentation (FBTS) dataset and the BraTS 2021 dataset (focused on Whole Tumor segmentation). The best-discovered architecture [64, 64, 64, 256, 64, 128, 256] achieved notable performance: on the FBTS dataset, it reached 0.9503 DSC and 0.9054 JI; on BraTS 2021, it obtained 0.9261 DSC and 0.8631 JI, outperforming several state-of-the-art methods. Convergence and segmentation-map evolution confirm that BO effectively guided the architectural search process. These findings demonstrate the potential of BO-driven deep learning in medical imaging, opening new avenues for architecture-level optimization with minimal manual intervention.
