Multi-Objective Image Fusion for Brain Tumor Detection Using Improved Weighted Quantum Firefly Optimization and StyleGAN-MAE-SwinViT
Bibliographic record
Abstract
To improve diagnostic precision, the accurate fusion of imaging methods is necessary for brain tumor identification from imaging studies.Conventional fusion techniques frequently encounter issues such as noise interference, low contrast, and data loss, which reduce their effectiveness in clinical settings.This paper proposes a Multi-Objective Image Fusion architecture that combines StyleGAN-MAE-ViT and Improved Weighted Quantum Firefly Optimization (IWQFO) to address these challenges.The IWQFO method employs a quantum-inspired searching process to balance multiple objectives, including brightness enhancement, edge preservation, and architectural resemblance, to optimize the fusion process.Meanwhile, StyleGAN-MAE-ViT integrates the advantages of the Vision Transformer (ViT) for spatial attention-based tumor segmentation, the Masked Autoencoder (MAE) for robust feature reconstruction, and StyleGAN for high-fidelity image generation.To preserve critical tumor information while eliminating redundant noise, the proposed architecture fuses multi-modal MRI images (T1, T2, and FLAIR).Experimental evaluations conducted on benchmark brain tumor datasets demonstrate that the proposed approach outperforms existing fusion techniques in terms of Peak Signal-to-Noise Ratio (PSNR), tumor segmentation accuracy, Feature Similarity Index (FSIM), and Structural Similarity Index (SSIM).These findings validate the superiority of the IWQFO-StyleGAN-MAE-ViT fusion model in enhancing tumor visibility, aiding radiologists in making accurate and timely diagnoses.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".