Brain Tumor Cnn Eval

---
name: brain-tumor-cnn-eval
description: Evaluates the ability of various CNN architectures (custom, U-Net, Fast R-CNN, and transfer learning models) to accurately classify brain tumors (glioma, meningioma, pituitary) from MRI images. It probes architectural robustness, generalization across data splits, and performance under class imbalance conditions. Use when the user wants to benchmark on Kaggle Brain Tumor Dataset, or asks about evaluating this task. Reports accuracy.
metadata:
  skill_kind: dataset_eval
  source_arxiv: 2509.05821
  bibtex_key: asghariilani2025braintumor
  confidence: high
---

# brain-tumor-cnn-eval

> Brain Tumor Detection Through Diverse CNN Architectures in IoT Healthcare Industries: Fast R-CNN, U-Net, Transfer Learning-Based CNN, and Fully Connected CNN — Asghari Ilani et al. (2025) (arXiv:2509.05821, 2025)

## What this evaluates

Evaluates the ability of various CNN architectures (custom, U-Net, Fast R-CNN, and transfer learning models) to accurately classify brain tumors (glioma, meningioma, pituitary) from MRI images. It probes architectural robustness, generalization across data splits, and performance under class imbalance conditions.

## Datasets

- **Kaggle Brain Tumor Dataset** — total ?; splits: train (-1), val (-1), test (-1)

## Metrics

- `accuracy` **(primary)** — range: percent
  - Percentage of correctly classified instances out of the total number of instances. Computed as (True Positives + True Negatives) / Total.
- `f1-score` — range: percent
  - Harmonic mean of precision and recall, computed per class and averaged (weighted).
- `precision` — range: percent
  - Ratio of true positive predictions to all positive predictions for each class.
- `recall` — range: percent
  - Ratio of true positive predictions to all actual positive instances for each class.
- `auc` — range: percent
  - Area under the Receiver Operating Characteristic curve, measuring the model's ability to distinguish between classes across all classification thresholds.

## Input / output format

**Input**: Grayscale MRI images resized to 270×270 pixels.

**Output**: Softmax class probabilities for three tumor categories (glioma, meningioma, pituitary).

## Scoring recipe

```python
def compute_metrics(y_true, y_pred, y_proba):
    acc = (y_true == y_pred).mean() * 100
    prec = precision_score(y_true, y_pred, average='weighted') * 100
    rec = recall_score(y_true, y_pred, average='weighted') * 100
    f1 = f1_score(y_true, y_pred, average='weighted') * 100
    auc = roc_auc_score(y_true, y_proba, multi_class='ovr') * 100
    return {'accuracy': acc, 'precision': prec, 'recall': rec, 'f1-score': f1, 'auc': auc}
```

## Common pitfalls

- The paper uses both a fixed 80/20 train-test split and 5-fold stratified cross-validation without clearly specifying which reported metric values correspond to which protocol.
- Class weights are mentioned for handling imbalance but the exact weighting scheme or formula is not provided, making exact reproduction difficult.
- The external cross-dataset validation excludes the 'normal' class, making direct comparison with the main 3-class results invalid.

## Evidence (verbatim from paper)

> The evaluation utilized a standardized dataset split into training (80%), validation (stratified cross-validation with k = 5), and testing (20%) subsets. The study also presents the accuracy, F-score, recall, precision, and AUC results obtained from these models in the study.

## Citation

```bibtex
@misc{asghariilani2025braintumor,
  title={Brain Tumor Detection Through Diverse CNN Architectures in IoT Healthcare Industries: Fast R-CNN, U-Net, Transfer Learning-Based CNN, and Fully Connected CNN},
  author={Asghari Ilani et al. (2025)},
  year={2025},
  note={arXiv:2509.05821}
}
```

- arXiv: 2509.05821