Grey Wolf Optimizer Enhances Adaptive Atrous Spatial Pyramid Pooling for Efficient Multi-Scale Feature Selection in Medical Image Segmentation
Bibliographic record
Abstract
This study proposes enhancing DeepLabV3+ by incorporating the Grey Wolf Optimizer (GWO) for adaptive channel selection into the Atrous Spatial Pyramid Pooling (ASPP) module. The proposed enhancement helps the model prioritize informative features, improving segmentation accuracy in complex scenarios like brain tumor detection. The modification in the Atrous Spatial Pyramid Pooling module as suggested in this study with incorporation of adaptive channels allows context-based feature capture at multiple scales necessary in order to achieve accurate segmentation. Analyses conducted on MRI dataset have proved that the addition of GWO improves the mean Intersection over Union (mIoU) score of the model to 75.8±0.8%, which is a remarkable improvement over the baseline score of 73.1±0.8% obtained by the baseline DeepLabV3+ model. In addition, the model achieves a greater Dice score of 82.7±1.3% as well as an accuracy rate of 99.300± 0.047%, outperforming rival models like FPN and FCN-ResAlexNet. The approach utilized in GWO facilitates the selection of highly relevant channels with minimal redundancy, thereby enhancing feature representation. The model also tackles essential challenges that relate to classification imbalance, hence maintaining a level of stability in a variety of circumstances. The addition of a two-stage convolution methodology with global context incorporation with images greatly improves the competence level of the model, therefore making it a viable alternative in real-time medical images. The study highlights possibilities in deeper models’ methodologies in order to attain improved competence in difficult segmentation.
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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) | 0.002 | 0.001 |
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".