# Brain Tumor Classification Eval

> brain-tumor-classification-eval

- Skill: `qhjqhj00/brain-tumor-classification-eval` (Agent Skill)
- Install (CLI): `npx skillmds@latest add qhjqhj00/brain-tumor-classification-eval`
- Raw SKILL.md: https://api.skillmd.com/api/skills/qhjqhj00/brain-tumor-classification-eval/raw
- Safety review: pending (external: skill-scanner PASS, skillspector PASS)
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- Author: qhjqhj00 (https://skillmd.com/u/qhjqhj00)
- Updated: 2026-09-21
- Page: https://skillmd.com/skills/qhjqhj00/brain-tumor-classification-eval

---


# brain-tumor-classification-eval

> Optimized Weighted Voting System for Brain Tumor Classification Using MRI Images — Vu et al. (2026) (arXiv:2603.28357, 2026)

## What this evaluates

Evaluates the ability of machine learning models to classify brain MRI images into four categories: glioma, meningioma, pituitary tumor, and no tumor. It tests feature extraction and decision fusion capabilities using both deep learning and traditional ML classifiers.

## Datasets

- **Kaggle Brain Tumor MRI dataset** — total 7023; splits: train (5712), test (1311); repo https://www.kaggle.com/datasets/masoudnickparvar/brain-tumor-mri-dataset
- **Figshare Brain Tumor dataset** — total 3064; splits: train (-1), test (-1); repo https://figshare.com/articles/dataset/brain_tumor_dataset/1512427

## Metrics

- `Accuracy` **(primary)** — range: percent
  - Ratio of correctly classified instances to the total number of instances, multiplied by 100.
- `F1-score` — range: [0, 1]
  - Harmonic mean of precision and recall: 2 * (precision * recall) / (precision + recall).

## Input / output format

**Input**: T1-weighted MRI images preprocessed with balance contrast enhancement, K-means clustering, and Canny edge detection.

**Output**: One of four class labels: 'glioma', 'meningioma', 'pituitary', or 'no tumor'.

## Scoring recipe

```python
def compute_accuracy(predictions, gold_labels):
    correct = sum(1 for p, g in zip(predictions, gold_labels) if p == g)
    return (correct / len(gold_labels)) * 100
```

## Common pitfalls

- Figshare dataset has an imbalanced class distribution, which can skew accuracy metrics compared to the balanced Kaggle dataset.
- The evaluation uses a fixed train-test split rather than k-fold cross-validation, potentially affecting generalizability estimates.
- Image preprocessing steps (edge detection, contrast enhancement) are applied before model input but are not standardized across all baseline comparisons.

## Evidence (verbatim from paper)

> Overall, models trained on the Kaggle dataset exhibited higher accuracy than those trained on Figshare, mainly due to Kaggle’s larger dataset size and more balanced class distribution.

## Citation

```bibtex
@misc{vu2026optimized,
  title={Optimized Weighted Voting System for Brain Tumor Classification Using MRI Images},
  author={Vu et al. (2026)},
  year={2026},
  note={arXiv:2603.28357}
}
```

- arXiv: 2603.28357