Enhancing Brain Tumor Classification Using Deep Learning
Bibliographic record
Abstract
This study aims to enhance the classification of brain tumors using advanced deep learning models applied to magnetic resonance imaging (MRI) images. The methodology involves applying pre-trained Convolutional Neural Networks (CNNs) including ResNet50, MobileNet, and Inception v2, and transformer models such as Vision Transformer (ViT) and Shifted Window (Swin). A hybrid model combining MobileNet and Swin was developed to improve classification accuracy. Data augmentation and image enhancement techniques were applied to optimize model performance. The models were rigorously evaluated using metrics such as accuracy, precision, recall, and F1-score. Among the models tested, MobileNet-Swin achieved the highest classification with accuracy of 99.65 and precision of 99.82 and recall of 99.82 and F1-score of 99.82. The findings support the effectiveness of hybrid deep learning models in assisting radiologists and improving clinical decision-making for brain tumor classification. Future work should focus on expanding the dataset and exploring additional deep learning architectures to further enhance classification accuracy.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| 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) | 0.001 | 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".