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README.md

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----
-license: mit
----
+---
+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)
+---