An Improved Brain Tumors Detection in MRI Images using Deep Learning Approach
Bibliographic record
Abstract
One of the most serious neurological conditions is brain tumors, and early and precise identification is essential to increasing patient survival. Although magnetic resonance imaging (MRI) offers comprehensive structural information, radiologists must manually interpret the results, which takes time and can be subjective. An enhanced multi-task deep learning system for brain tumor analysis is presented in this research. It uses a single architecture to conduct both tumor segmentation and multi-class subtype classification (glioma, meningioma, pituitary, and no-tumor). By integrating an EfficientNet-B0 encoder with a U-Net-based segmentation decoder and a classification head, the suggested model allows the network to concentrate on tumor-relevant regions while preserving interpretability through Grad-CAM visuals. To improve generalization and lessen overfitting, extensive preprocessing was used, including normalization, scaling, and data augmentation. According to experimental results, the model outperforms traditional single-task methods with a 96.2% classification accuracy and a Dice similarity coefficient of 0.91. The study demonstrates the potential of multi-task deep learning frameworks as trustworthy decision-support tools for radiologists, enabling quicker and more accurate MRI-based brain tumor identification in clinical situations.
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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 | 0.001 |
| 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".