# Brain Tumor Detection Eval

> Evaluates a deep learning model's ability to classify brain MRI images into four tumor categories (glioma, meningioma, no tumor, pituitary). It probes multi-class image classification performance, generalization to unseen medical scans, and the model's capacity to balance precision and recall across classes. Use when the user wants to benchmark on Public MRI dataset (unspecified), or asks about evaluating this task. Reports accuracy.

- Skill: `qhjqhj00/brain-tumor-detection-eval` (Agent Skill)
- Install (CLI): `npx skillmds add qhjqhj00/brain-tumor-detection-eval`
- Raw SKILL.md: https://api.skillmd.com/api/skills/qhjqhj00/brain-tumor-detection-eval/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: AI & ML
- Author: qhjqhj00 (https://skillmd.com/u/qhjqhj00)
- Updated: 2026-09-08
- Page: https://skillmd.com/skills/qhjqhj00/brain-tumor-detection-eval

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# brain-tumor-detection-eval

> Deep Brain Net: An Optimized Deep Learning Model for Brain tumor Detection in MRI Images Using EfficientNetB0 and ResNet50 with Transfer Learning — Onah et al. (2025) (arXiv:2507.07011, 2025)

## What this evaluates

Evaluates a deep learning model's ability to classify brain MRI images into four tumor categories (glioma, meningioma, no tumor, pituitary). It probes multi-class image classification performance, generalization to unseen medical scans, and the model's capacity to balance precision and recall across classes.

## Datasets

- **Public MRI dataset (unspecified)** — total ?; splits: train (-1), val (-1)

## Metrics

- `accuracy` **(primary)** — range: [0, 1]
  - Proportion of correctly classified instances out of the total number of instances.
- `weighted F1-score` — range: [0, 1]
  - Harmonic mean of precision and recall, averaged across classes weighted by the number of true instances per class.
- `macro-average AUC-ROC` — range: [0, 1]
  - Area under the Receiver Operating Characteristic curve computed for each class using a one-vs-rest approach, then averaged unweighted across all classes.

## Input / output format

**Input**: Grayscale MRI images converted to RGB via channel stacking, resized to 224×224 pixels, and standardized using EfficientNet's normalization scheme.

**Output**: Predicted tumor class label from four categories: Glioma tumor, Meningioma tumor, No tumor, Pituitary tumor.

## Scoring recipe

```python
def compute_metrics(y_true, y_pred, y_prob, num_classes=4):
    accuracy = (y_true == y_pred).mean()
    precision, recall, f1 = precision_recall_fscore_support(y_true, y_pred, average=None)
    weighted_f1 = f1_score(y_true, y_pred, average='weighted')
    auc_roc = roc_auc_score(y_true, y_prob, multi_class='ovr', average='macro')
    return {'accuracy': accuracy, 'weighted_f1': weighted_f1, 'macro_auc_roc': auc_roc}
```

## Common pitfalls

- The paper reports conflicting validation accuracy values (94.7% vs 93.2%) without clarifying which corresponds to the final classification report.
- No explicit train/validation/test split sizes or ratios are provided, making reproducibility of data partitioning difficult.
- Grayscale images are converted to 3-channel RGB for pre-trained backbones, which may introduce redundant channel information not accounted for in standard ImageNet pre-training protocols.

## Evidence (verbatim from paper)

> The classification report, shown in Table III below, summarizes the model's performance in each class using three key metrics: precision, recall and F1 score. ... The weighted F1-score across all classes, weighted by the number of true instances per class, is 0.88. ... Additionally, the AUC-ROC score provides insight into the model's ability to distinguish between classes across different threshold values, offering a comprehensive measure of classification performance. The macro-average AUC-ROC score is 0.98...

## Citation

```bibtex
@misc{onah2025deepbrainnet,
  title={Deep Brain Net: An Optimized Deep Learning Model for Brain tumor Detection in MRI Images Using EfficientNetB0 and ResNet50 with Transfer Learning},
  author={Onah et al. (2025)},
  year={2025},
  note={arXiv:2507.07011}
}
```

- arXiv: 2507.07011