Optimized DeepLabV3+ for Clinical Data Analysis through Advanced Particle Swarm Optimization‐Based Channel Selection
Bibliographic record
Abstract
Medical image analysis of complex neurological diseases, such as brain tumors and Alzheimer's disease, is challenging due to subtle pathological features. Traditional deep learning models often extract redundant features that hinder segmentation accuracy. To address this limitation, a novel machine‐learning framework is proposed that combines an Extended Exploration Particle Swarm Optimization (EE‐PSO) algorithm with a modified DeepLabV3+ architecture to enhance feature selection and improve segmentation performance in medical imaging tasks. The two main contributions are 1) a structurally optimized DeepLabV3+ model that uses dynamic EE‐PSO‐driven channels instead of standard convolutional layers to adaptively prioritize important features during training, and 2) an improved PSO algorithm that incorporates particle reinitialization and adaptive inertia weight adjustment to reduce premature convergence and enhance global search capabilities. The atrous spatial pyramid pooling module has the EE‐PSO component strategically incorporated inside it, allowing for the synergistic integration of multi‐scale contextual information with optimal feature maps. The system demonstrates improvements in mean intersection over union (mIOU) of 2.7% and 2.8% when tested on Alzheimer's and brain tumor datasets. Through the integration of deep feature learning, this study improves the precision‐autonomy trade‐off in medical image analysis.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".