Brain Tumor Segmentation using Osprey Optimization Assisted DeepLabV3+ Model
Bibliographic record
Abstract
Brain tumors are among the frequently diagnosed malignant conditions across all age groups. Accurately determining their grade has a major challenge for radiologists in clinical assessments and automatic diagnostic systems. Identifying tumor types and implementing preventive measures remains one of the most complex processes of brain tumor classification. Various Deep Learning (DL) models are proposed in the existing approaches for enhancing the accuracy of brain tumor classification. But, the challenges like training time and complexity are occurred in these works. To tackle these issues, this work presents a Enhanced DeepLabV3+ to segment and categorize brain tumor. At first, non-local means (NLM) filtering is utilized for pre-processing for reducing noise and preserving essential structural details. Then, the Enhanced DeepLabV3+ is employed for segmentation, with AlexNet is the backbone for segmentation tasks. To further refine the segmentation process, hyperparameter optimization of the DeepLabV3 architecture is conducted using the Osprey Optimization Algorithm (OOA) approach and provide significant improvements in brain tumor segmentation performance. The evaluation is performed on the Brain TCIA and Figshare datasets and achieved better accuracies of 98.97% and 99.23% respectively.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".