Szczegóły publikacji
Opis bibliograficzny
Improved brain tumor segmentation using Modified U-Net based on Particle Swarm Optimization Image Enhancement / Shoffan SAIFULLAH, Rafał DREŻEWSKI // W: GECCO'24 Companion [Dokument elektroniczny] : proceedings of the Genetic and Evolutionary Computation Conference Companion : Melbourne, Australia, July 14-18, 2024 / Association for Computing Machinery. — Wersja do Windows. — Dane tekstowe. — New York : Association for Computing Machinery, cop. 2024. — e-ISBN: 979-8-4007-0495-6. — S. 611-614. — Wymagania systemowe: Adobe Reader. — Tryb dostępu: https://dl.acm.org/doi/pdf/10.1145/3638530.3654339 [2024-08-05]. — Bibliogr. s. 614, Abstr. — S. Saifullah - dod. afiliacja: Universitas Pembangunan Nasional Veteran Yogyakarta
Autorzy (2)
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 154815 |
|---|---|
| Data dodania do BaDAP | 2024-09-02 |
| DOI | 10.1145/3638530.3654339 |
| Rok publikacji | 2024 |
| Typ publikacji | materiały konferencyjne (aut.) |
| Otwarty dostęp |  |
| Wydawca | Association for Computing Machinery (ACM) |
| Konferencja | Genetic and Evolutionary Computations 2024 |
Abstract
This study introduces a robust methodology, a Modified U-Net with Particle-Swarm-Optimization-based Image Enhancement, to address the complexities of brain tumor segmentation. Leveraging PSO-based Image Enhancement's adaptive features, our approach achieves superior performance on a dataset of 3064 Brain MRI images, boasting an accuracy of 99.93%, minimal loss (0.0015), and impressive Dice (0.9699) and Jaccard index (0.9421) values for overall images. The method significantly improves segmentation accuracy, as evidenced by the increase of 9.37 p.p. in Dice and 5.3 p.p in the Jaccard index compared to the U-Net basic approach. Comparative analysis with other methods, including Modified U-Net variants, LinkNet, SegNet, Active Contour, and Fuzzy C-Means, consistently demonstrates outperformance. This method advances medical image analysis by providing precise segmentation and paves the way for future research into optimization and extensions for diverse medical imaging applications.
