Update README.md

README.md

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----
-license: mit
----
+---
+license: mit
+metrics:
+- accuracy
+pipeline_tag: image-segmentation
+tags:
+- medical
+- biology
+---
+# CT Heart Segmentation Model
+
+**Model Information:**
+- **Architecture:** U-Net
+- **Task:** Binary segmentation of heart in CT scans
+- **Dataset:** [Heart CT Dataset](https://www.kaggle.com/datasets/nikhilroxtomar/ct-heart-segmentation)
+- **Input Size:** 224×224 RGB images
+
+**Performance Metrics:**
+- **Best Dice Score:** 0.9479
+- **Best IoU Score:** 0.9014
+
+**Usage:**
+```python
+from shifaa.vision import VisionModelFactory
+
+model = VisionModelFactory.create_model(
+    model_type="segmentation",
+    model_name="CT_Heart"
+)
+
+results = model.run("heart_ct.png", show_image=True)
+image = results["image"]
+mask = results["predicted_mask"]
+```
+
+**Sample Results:**
+![Sample Results](./CT_H_seg.png)
+
+
+**Architecture Details:**
+- Encoder: 4 downsampling blocks (Conv → BatchNorm → ReLU → Dropout)
+- Bottleneck: Deepest convolutional block
+- Decoder: 4 upsampling blocks with skip connections
+- Output: 1 channel with sigmoid activation
+
+**Preprocessing:**
+- Random horizontal flip
+- Random rotation ±15°
+- Random brightness & contrast adjustment
+- Normalize and convert to tensor
+
+**Training Details:**
+- **Loss Function:** Combined Dice Loss + BCE Loss
+- **Optimizer:** Adam (lr=0.001, weight_decay=1e-5)
+- **Batch Size:** 8
+- **Epochs:** 100 (with early stopping)
+
+---