---
title: Brain Tumor MRI Classifier
emoji: 🧠
colorFrom: indigo
colorTo: purple
sdk: gradio
sdk_version: 5.29.0
app_file: app.py
pinned: false
license: mit
tags:
  - medical-imaging
  - brain-tumor
  - efficientnet
  - image-classification
  - pytorch
  - mri
---

# Brain Tumor MRI Classifier — EfficientNet-B3

A fine-tuned **EfficientNet-B3** model for 4-class brain tumor classification from MRI scans, achieving **98.98% validation accuracy** and **0.9896 macro F1**.

## Classes

| Class | Description |
|---|---|
| Glioma | Tumor originating in glial cells of the brain or spine |
| Meningioma | Tumor arising from the meninges surrounding the brain |
| Pituitary Tumor | Tumor in the pituitary gland at the base of the brain |
| No Tumor | No tumor detected in the MRI scan |

## Model

- **Architecture**: EfficientNet-B3 (ImageNet pretrained) with custom classification head
- **Head**: `Dropout → Linear(1536, 512) → SiLU → Dropout → Linear(512, 4)`
- **Input size**: 300 × 300
- **Training**: Two-phase — backbone frozen for 5 epochs (head LR 1e-3), then full fine-tune with differential LR (backbone 1e-4, head 1e-3)
- **Schedule**: Cosine decay with 3-epoch linear warmup
- **Loss**: Class-weighted cross-entropy

## Weights

The model weights (`model.pt`) are hosted in this repository and downloaded automatically on first run via `huggingface_hub`.

To download manually:

```python
from huggingface_hub import hf_hub_download
ckpt_path = hf_hub_download(repo_id="your-hf-username/brain-tumor-efficientnet-b3", filename="model.pt")
```

## Dataset

Trained on a merged dataset from two sources:

- **Figshare Brain Tumor Dataset** — glioma, meningioma, pituitary MRI scans
- **Kaggle Brain Tumor MRI Dataset** — 4-class dataset with glioma, meningioma, pituitary, no tumor

| Split | Images |
|---|---|
| Train | 8,211 |
| Validation | 2,053 |

## Results

| Metric | Score |
|---|---|
| Accuracy | 0.9898 |
| Macro F1 | 0.9896 |
| Weighted F1 | 0.9898 |

Per-class F1: Glioma 0.9915 · Meningioma 0.9832 · No Tumor 0.9903 · Pituitary 0.9935

## Usage

```python
import torch
import torch.nn as nn
from torchvision import transforms
from torchvision.models import efficientnet_b3
from huggingface_hub import hf_hub_download
from PIL import Image

class EfficientNetClassifier(nn.Module):
    def __init__(self, num_classes=4, dropout=0.4):
        super().__init__()
        self.backbone = efficientnet_b3(weights=None)
        in_features = self.backbone.classifier[1].in_features
        self.backbone.classifier = nn.Sequential(
            nn.Dropout(p=dropout, inplace=True),
            nn.Linear(in_features, 512),
            nn.SiLU(),
            nn.Dropout(p=dropout / 2),
            nn.Linear(512, num_classes),
        )
    def forward(self, x):
        return self.backbone(x)

# Load
ckpt_path = hf_hub_download("your-hf-username/brain-tumor-efficientnet-b3", "model.pt")
ckpt = torch.load(ckpt_path, map_location="cpu", weights_only=False)
id_to_label = {int(k): v for k, v in ckpt["id_to_label"].items()}

model = EfficientNetClassifier()
model.load_state_dict(ckpt["model"])
model.eval()

# Infer
transform = transforms.Compose([
    transforms.Resize((300, 300)),
    transforms.ToTensor(),
    transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
])

img = Image.open("mri_scan.jpg").convert("RGB")
with torch.no_grad():
    probs = torch.softmax(model(transform(img).unsqueeze(0)), dim=-1)[0]
    pred  = id_to_label[probs.argmax().item()]

print(pred)
```

## Disclaimer

This model is intended for **research purposes only** and is not a certified medical diagnostic tool. Do not use for clinical decision-making.