---
license: mit
pipeline_tag: image-segmentation
tags:
- medical
- biology
---

# Skin Cancer Segmentation Model

**Model Information:**
- **Architecture:** U-Net
- **Task:** Binary segmentation of skin lesions in dermoscopy images
- **Dataset:** [Skin Lesion Mask Dataset](https://www.kaggle.com/datasets/surajghuwalewala/ham1000-segmentation-and-classification)
- **Input Size:** 128×128 grayscale images

**Performance Metrics:**
- **Best Dice Score:** 0.9175


**Classes:**
- Background (0)
- Lesion (1)

**Usage:**
```python
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:**
![Sample Results](./SC_seg.png)

**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)

---