Bio-Inspired Metaheuristics in Deep Learning for Brain Tumor Segmentation: A Decade of Advances and Future Directions DOI Creative Commons
Shoffan Saifullah, Rafał Dreżewski, Anton Yudhana

и другие.

Information, Год журнала: 2025, Номер 16(6), С. 456 - 456

Опубликована: Май 29, 2025

Accurate segmentation of brain tumors in magnetic resonance imaging (MRI) remains a challenging task due to heterogeneous tumor structures, varying intensities across modalities, and limited annotated data. Deep learning has significantly advanced accuracy; however, it often suffers from sensitivity hyperparameter settings generalization. To overcome these challenges, bio-inspired metaheuristic algorithms have been increasingly employed optimize various stages the deep pipeline—including tuning, preprocessing, architectural design, attention modulation. This review systematically examines developments 2015 2025, focusing on integration nature-inspired optimization methods such as Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Differential Evolution (DE), Grey Wolf Optimizer (GWO), Whale (WOA), novel hybrids including CJHBA BioSwarmNet into learning-based frameworks. A structured multi-query search strategy was executed using Publish or Perish Google Scholar Scopus databases. Following PRISMA guidelines, 3895 records were screened through automated filtering manual eligibility checks, yielding curated set 106 primary studies. Through bibliometric mapping, methodological synthesis, performance analysis, we highlight trends algorithm usage, application domains (e.g., architecture search), outcomes measured by metrics Dice Similarity Coefficient (DSC), Jaccard Index (JI), Hausdorff Distance (HD), ASSD. Our findings demonstrate that enhances accuracy robustness, particularly multimodal involving FLAIR T1CE modalities. The concludes identifying emerging research directions hybrid optimization, real-time clinical applicability, explainable AI, providing roadmap for future exploration this interdisciplinary domain.

Язык: Английский

Bio-Inspired Metaheuristics in Deep Learning for Brain Tumor Segmentation: A Decade of Advances and Future Directions DOI Creative Commons
Shoffan Saifullah, Rafał Dreżewski, Anton Yudhana

и другие.

Information, Год журнала: 2025, Номер 16(6), С. 456 - 456

Опубликована: Май 29, 2025

Accurate segmentation of brain tumors in magnetic resonance imaging (MRI) remains a challenging task due to heterogeneous tumor structures, varying intensities across modalities, and limited annotated data. Deep learning has significantly advanced accuracy; however, it often suffers from sensitivity hyperparameter settings generalization. To overcome these challenges, bio-inspired metaheuristic algorithms have been increasingly employed optimize various stages the deep pipeline—including tuning, preprocessing, architectural design, attention modulation. This review systematically examines developments 2015 2025, focusing on integration nature-inspired optimization methods such as Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Differential Evolution (DE), Grey Wolf Optimizer (GWO), Whale (WOA), novel hybrids including CJHBA BioSwarmNet into learning-based frameworks. A structured multi-query search strategy was executed using Publish or Perish Google Scholar Scopus databases. Following PRISMA guidelines, 3895 records were screened through automated filtering manual eligibility checks, yielding curated set 106 primary studies. Through bibliometric mapping, methodological synthesis, performance analysis, we highlight trends algorithm usage, application domains (e.g., architecture search), outcomes measured by metrics Dice Similarity Coefficient (DSC), Jaccard Index (JI), Hausdorff Distance (HD), ASSD. Our findings demonstrate that enhances accuracy robustness, particularly multimodal involving FLAIR T1CE modalities. The concludes identifying emerging research directions hybrid optimization, real-time clinical applicability, explainable AI, providing roadmap for future exploration this interdisciplinary domain.

Язык: Английский

Процитировано

0