README.md

---
base_model: google/medgemma-4b-it
library_name: transformers
model_name: medgemma-brain-cancer
tags:
- generated_from_trainer
- trl
- sft
- medical
- mri
- brain_tumor
licence: license
license: apache-2.0
language:
- en
pipeline_tag: image-text-to-text
metrics:
- accuracy
- f1
model-index:
  - name: finetuned-model
    results:
      - task:
          type: image-text-to-text
        dataset:
          name: orvile/brain-cancer-mri-dataset
          type: image-text-to-text
        metrics:
          - name: accuracy
            type: accuracy
            value: 0.8927392739273927
          - name: f1
            type: f1
            value: 0.892641793935792
---

# 🧠 MedGemma-Brain-Cancer

`medgemma-brain-cancer` is a fine-tuned version of [google/medgemma-4b-it](https://huggingface.co/google/medgemma-4b-it), trained specifically for brain tumor diagnosis and classification from MRI scans. This model leverages vision-language learning for enhanced medical imaging interpretation.

## 🔬 Model Details

* **Base Model**: [google/medgemma-4b-it](https://huggingface.co/google/medgemma-4b-it)
* **Dataset**: [orvile/brain-cancer-mri-dataset](https://www.kaggle.com/datasets/orvile/brain-cancer-mri-dataset)
* **Fine-tuning Approach**: Supervised fine-tuning (SFT) using [Transformers Reinforcement Learning (TRL)](https://github.com/huggingface/trl)
* **Task**: Brain tumor classification from MRI images
* **Pipeline Tag**: `image-text-to-text`
* **Accuracy Improvement**:

  * Base model accuracy: **33%**
  * Fine-tuned model accuracy: **89%**

## 📊 Results & Notebook

Explore the training pipeline, evaluation results, and experiments in the notebook:

👉 **[Fine\_tuning\_MedGemma.ipynb](https://huggingface.co/kingabzpro/medgemma-brain-cancer/blob/main/Fine_tuning_MedGemma.ipynb)**

## 🚀 Inference Example

```python
# pip install transformers accelerate
from transformers import AutoProcessor, AutoModelForImageTextToText
from PIL import Image
import requests
import torch

model_id = "kingabzpro/medgemma-brain-cancer"

model = AutoModelForImageTextToText.from_pretrained(
    model_id,
    torch_dtype=torch.bfloat16,
    device_map="auto",
)
processor = AutoProcessor.from_pretrained(model_id)

# Example Brain MRI image — attribution: Orvile, via Kaggle dataset
image_url = "https://storage.googleapis.com/kagglesdsdata/datasets/7006196/11239552/Brain_Cancer%20raw%20MRI%20data/Brain_Cancer/brain_menin/brain_menin_0002.jpg?X-Goog-Algorithm=GOOG4-RSA-SHA256&X-Goog-Credential=databundle-worker-v2%40kaggle-161607.iam.gserviceaccount.com%2F20250527%2Fauto%2Fstorage%2Fgoog4_request&X-Goog-Date=20250527T102729Z&X-Goog-Expires=345600&X-Goog-SignedHeaders=host&X-Goog-Signature=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"
image = Image.open(requests.get(image_url, headers={"User-Agent": "example"}, stream=True).raw)

messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "text": None, "image": image},
            {"type": "text", "text": "What is the most likely type of brain cancer shown in the MRI image?\nA: brain glioma\nB: brain menin\nC: brain tumor"}
        ]
    }
]

inputs = processor.apply_chat_template(
    messages, add_generation_prompt=True, tokenize=True,
    return_dict=True, return_tensors="pt"
).to(model.device, dtype=torch.bfloat16)

input_len = inputs["input_ids"].shape[-1]

with torch.inference_mode():
    generation = model.generate(**inputs, max_new_tokens=20, do_sample=False)
    generation = generation[0][input_len:]

decoded = processor.decode(generation, skip_special_tokens=True)
print(decoded)
```

**Expected Output:**

```text
B: brain menin
```

## 🧪 Intended Use

This model is intended for research and educational purposes related to medical imaging, specifically brain tumor classification. It is **not** a certified diagnostic tool and should not be used in clinical decision-making without further validation.

## 🏷️ Tags

* `medical`
* `brain_tumor`
* `mri`
* `trl`
* `sft`

## 📜 License

Apache 2.0 License