---
tags:
- image-classification
- medical-imaging
- brain-tumor
- resnet
- pytorch
license: apache-2.0
datasets:
- sartajbhuvaji/brain-tumor-classification-mri
metrics:
- accuracy
model-index:
- name: brain-tumor-resnet-classifier
  results:
  - task:
      type: image-classification
    dataset:
      name: Brain Tumor Classification MRI
      type: sartajbhuvaji/brain-tumor-classification-mri
    metrics:
    - type: accuracy
      value: 79.95
---

# Brain Tumor Classification with ResNet

## Model Description

Bu model, beyin MRI görüntülerinden tümör sınıflandırması yapmak için eğitilmiş bir ResNet modelidir.

## Dataset

Model, [Brain Tumor Classification MRI](https://www.kaggle.com/datasets/sartajbhuvaji/brain-tumor-classification-mri) veri seti üzerinde eğitilmiştir.

Veri seti 4 sınıf içermektedir:
- Glioma
- Meningioma 
- No Tumor
- Pituitary

## Training Details

**En İyi Model Konfigürasyonu:**
- Model: resnet101
- Test Accuracy: **79.95%**
- Validation Accuracy: 97.56%

## Denenen Konfigürasyonlar ve Sonuçları

| Model | Augmentation | Optimizer | Test Acc |
|-------|-------------|-----------|----------|
| resnet50 | heavy | - | 76.65% |
| resnet101 | best | - | 79.95% |


## Kullanım
```python
import torch
from torchvision import transforms, models
from PIL import Image

# Model yükleme
model = models.resnet101(pretrained=False)
num_features = model.fc.in_features
model.fc = torch.nn.Linear(num_features, 4)

checkpoint = torch.hub.load_state_dict_from_url(
    'https://huggingface.co/Yasette/brain-tumor-resnet-classifier/resolve/main/pytorch_model.pth',
    map_location='cpu'
)
model.load_state_dict(checkpoint['model_state_dict'])
model.eval()

# Görüntü işleme
transform = transforms.Compose([
    transforms.Resize((224, 224)),
    transforms.ToTensor(),
    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
])

# Tahmin
image = Image.open('brain_mri.jpg').convert('RGB')
input_tensor = transform(image).unsqueeze(0)

with torch.no_grad():
    output = model(input_tensor)
    _, predicted = torch.max(output, 1)
    
classes = ['glioma', 'meningioma', 'notumor', 'pituitary']
print(f"Tahmin: {classes[predicted.item()]}")
```

## Ekip Üyeleri

- [İsim 1]
- [İsim 2]
- [İsim 3]

## Lisans

Apache 2.0