Ahmed-Selem/Shifaa-Skin-Cancer-UNet-Segmentation

Skin Cancer Segmentation Model

Model Information:

• Architecture: U-Net
• Task: Binary segmentation of skin lesions in dermoscopy images
• Dataset: Skin Lesion Mask Dataset
• Input Size: 128×128 grayscale images

Performance Metrics:

• Best Dice Score: 0.9175

Classes:

• Background (0)
• Lesion (1)

Usage:

from shifaa.vision import VisionModelFactory

model = VisionModelFactory.create_model(
    model_type="segmentation",
    model_name="Skin_Cancer"
)

results = model.run("skin_lesion.jpg", show_image=True)
image = results["image"]
mask = results["predicted_mask"]

Sample Results:

Architecture Details:

• Encoder: 4 blocks (1→16→32→64→128 channels)
• Bottleneck: 256 filters
• Decoder: 4 blocks with skip connections
• Final layer: 1 output channel with sigmoid activation

Preprocessing:

• Images normalized to [0, 1]
• Binary masks (0=background, 1=lesion)
• Convert to tensors: shape (B, 1, H, W)

Training Details:

• Loss Function: Binary Cross-Entropy Loss
• Optimizer: Adam (lr=0.001)
• Batch Size: 16
• Epochs: 80 (with early stopping after 20 epochs)

---