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@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+img.png filter=lfs diff=lfs merge=lfs -text

Tumor.py

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+import torch
+import torch.nn as nn
+import torch.optim as optim
+from torch.utils.data import Dataset, DataLoader
+import torchvision.transforms as transforms
+import torchvision.models as models
+from datasets import load_dataset
+
+# Device setup
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+# Load dataset
+ds = load_dataset("sartajbhuvaji/Brain-Tumor-Classification")
+train_ds = ds["Training"]
+test_ds = ds["Testing"]
+
+# Data augmentation for training
+train_transform = transforms.Compose([
+    transforms.Resize((224, 224)),
+    transforms.RandomHorizontalFlip(),
+    transforms.RandomRotation(15),
+    transforms.ColorJitter(brightness=0.2, contrast=0.2),
+    transforms.ToTensor(),
+    transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
+])
+# Simpler transform for testing
+test_transform = transforms.Compose([
+    transforms.Resize((224, 224)),
+    transforms.ToTensor(),
+    transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
+])
+
+# Custom Dataset to wrap Hugging Face dataset for PyTorch usage
+class HFToTorchDataset(Dataset):
+    def __init__(self, hf_ds, transform=None):
+        self.hf_ds = hf_ds
+        self.transform = transform
+    def __len__(self):
+        return len(self.hf_ds)
+    def __getitem__(self, idx):
+        image = self.hf_ds[idx]["image"].convert("RGB")
+        label = self.hf_ds[idx]["label"]
+        if self.transform:
+            image = self.transform(image)
+        return image, label
+
+torch_train = HFToTorchDataset(train_ds, train_transform)
+torch_test = HFToTorchDataset(test_ds, test_transform)
+
+train_loader = DataLoader(torch_train, batch_size=32, shuffle=True)
+test_loader = DataLoader(torch_test, batch_size=32, shuffle=False)
+
+# Load pretrained ResNet18 and adjust for 4 classes
+model = models.resnet18(weights="IMAGENET1K_V1")
+num_ftrs = model.fc.in_features
+model.fc = nn.Linear(num_ftrs, 4)
+model = model.to(device)
+
+# Loss and optimizer
+criterion = nn.CrossEntropyLoss()
+optimizer = optim.Adam(model.parameters(), lr=0.0005)
+
+# Train!
+num_epochs = 10  # Increase for even better results
+for epoch in range(num_epochs):
+    model.train()
+    running_loss = 0.0
+    correct = total = 0
+    for images, labels in train_loader:
+        images, labels = images.to(device), labels.to(device)
+        optimizer.zero_grad()
+        outputs = model(images)
+        loss = criterion(outputs, labels)
+        loss.backward()
+        optimizer.step()
+        running_loss += loss.item() * images.size(0)
+        _, preds = torch.max(outputs, 1)
+        correct += (preds == labels).sum().item()
+        total += labels.size(0)
+    epoch_loss = running_loss / total
+    accuracy = correct / total
+    print(f"Epoch {epoch+1}/{num_epochs}, Loss: {epoch_loss:.4f}, Accuracy: {accuracy:.4f}")
+
+# Test accuracy
+model.eval()
+correct = total = 0
+with torch.no_grad():
+    for images, labels in test_loader:
+        images, labels = images.to(device), labels.to(device)
+        outputs = model(images)
+        _, preds = torch.max(outputs, 1)
+        correct += (preds == labels).sum().item()
+        total += labels.size(0)
+print(f"Test Accuracy: {correct / total:.4f} ({correct}/{total})")
+
+torch.save(model.state_dict(), "brain_tumor_resnet18.pth")
+print("Model weights saved to brain_tumor_resnet18.pth")

brain_tumor_resnet18.pth

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+version https://git-lfs.github.com/spec/v1
+oid sha256:ebd3aabb2861ddee9dd99f3e1adec967876137c41b8c529b57ce14c252ab64cf
+size 44793419

tumor_test.py

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+import torch
+import torch.nn as nn
+from torchvision import transforms, models
+from PIL import Image
+
+# Device setup
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+# Transform (must match training on ResNet18/224 RGB)
+transform = transforms.Compose([
+    transforms.Resize((224, 224)),
+    transforms.ToTensor(),
+    transforms.Normalize([0.485, 0.456, 0.406],
+                         [0.229, 0.224, 0.225])
+])
+
+# Label map (matches Hugging Face dataset)
+label_map = {
+    0: "glioma",
+    1: "meningioma",
+    2: "no_tumor",
+    3: "pituitary"
+}
+
+# ResNet18 model (final layer for 4 classes)
+model = models.resnet18(weights=None)
+num_ftrs = model.fc.in_features
+model.fc = nn.Linear(num_ftrs, 4)
+model = model.to(device)
+model.load_state_dict(torch.load("brain_tumor_resnet18.pth", map_location=device))
+model.eval()
+
+# Predict function
+def predict_image(image_path):
+    img = Image.open(image_path).convert('RGB')
+    img = transform(img).unsqueeze(0).to(device)  # batch size 1
+    with torch.no_grad():
+        outputs = model(img)
+        _, pred_idx = torch.max(outputs, 1)
+        pred_label = label_map[pred_idx.item()]
+    print(f"Prediction: {pred_label}")
+
+# Example usage:
+predict_image("D:\\python\\Advanced_tumor\\img_3.png")  # Replace with your image