README.md

metadata

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 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

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