A novel hybrid model for brain tumor analysis with CNN and Moth Flame Optimization
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Early and accurate detection of brain tumors is vital for improving patient outcomes and treatment decisions. This study presents a Hybrid Brain Tumor Analysis (BTA) framework that integrates Moth Flame Optimization (MFO) and Convolutional Neural Networks (CNNs) for tumor identification, segmentation, and classification using MRI scans. A hybrid segmentation approach is employed, combining K-means clustering with MFO and a custom fitness function to extract tumor regions. Feature extraction is followed by MFO-based feature selection to reduce dimensionality and enhance classification performance. The refined features are used to train a custom CNN architecture, BTA-Net, for classifying tumors into meningioma, glioma, and pituitary types. The proposed model achieves a 3.22 % improvement in classification accuracy compared to baseline methods, along with notable gains in precision (4.07 %), recall (2.46 %), and F-measure (3.25 %). Statistical validation confirms the significance of these results, making the BTA framework a robust tool for automated brain tumor analysis.
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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 | 0.000 | 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 | 0.000 |
| 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 it