Update README.md

README.md

@@ -1,6 +1,122 @@
 ---
-license: cc-by-nc-3.0
+license: cc-by-nc-4.0
+datasets:
+  - chest-xray-pneumonia
+library_name: PyTorch
 tags:
-- custom
-- CNN
+  - pneumonia-detection
+  - cnn
+  - medical-imaging
+  - binary-classification
+  - chest-xray
+  - healthcare
+  - pytorch
+model-index:
+  - name: ImprovedPneumoniaCNN
+    results:
+      - task:
+          type: image-classification
+          name: Pneumonia Detection
+        metrics:
+          - name: Accuracy
+            type: accuracy
+            value: 0.9676
+          - name: F1 Score
+            type: f1
+            value: 0.9685
+          - name: AUC
+            type: auc
+            value: 0.9959
+          - name: Loss
+            type: loss
+            value: 0.0778
+---
+
+# 🧠 ImprovedPneumoniaCNN: Pneumonia Detection from Chest X-rays
+
+This repository hosts `ImprovedPneumoniaCNN`, a custom Convolutional Neural Network model designed to detect **Pneumonia** from chest X-ray images. It incorporates enhancements like dropout, batch normalization, SiLU activation, and Convolutional Block Attention Module (CBAM) for improved robustness and generalization.
+
+---
+
+## 📊 Evaluation Results
+
+| Metric   | Score   |
+|----------|---------|
+| Accuracy | 96.76%  |
+| F1 Score | 0.9685  |
+| AUC      | 0.9959  |
+| Loss     | 0.0778  |
+
+---
+
+### Confusion Matrix
+
+[[1680 42]
+[ 74 1782]]
+
+---
+
+### Classification Report
+
+| Class     | Precision | Recall | F1-Score | Support |
+|-----------|-----------|--------|----------|---------|
+| Normal    | 0.96      | 0.98   | 0.97     | 1722    |
+| Pneumonia | 0.98      | 0.96   | 0.97     | 1856    |
+
+---
+
+## 🏗️ Architecture Highlights
+
+- Custom CNN with residual blocks
+- Uses **CBAM** attention for spatial and channel refinement
+- **SiLU** activation for better non-linearity
+- **Dropout** and **BatchNorm** for regularization
+- Final **Global Average Pooling** + FC layer
+
+---
+
+## 🚀 How to Use
+
+### 🔧 Install Dependencies
+
+```bash
+pip install torch torchvision albumentations scikit-learn matplotlib seaborn
+import torch
+from torchvision import transforms
+from PIL import Image
+from model import ImprovedPneumoniaCNN  # make sure model is defined/imported
+
+# Load model
+model = ImprovedPneumoniaCNN()
+model.load_state_dict(torch.load("improved_pneumonia_cnn.pth", map_location=torch.device('cpu')))
+model.eval()
+
+# Preprocess image
+transform = transforms.Compose([
+    transforms.Grayscale(),
+    transforms.Resize((224, 224)),
+    transforms.ToTensor(),
+])
+
+img = Image.open("path_to_chest_xray.jpg")
+img_tensor = transform(img).unsqueeze(0)
+
+# Predict
+with torch.no_grad():
+    output = model(img_tensor)
+    prediction = torch.sigmoid(output).item()
+    print("Pneumonia" if prediction > 0.5 else "Normal")
+```
+---
+
+## Contributors
+- [Thiyaga158](https://huggingface.co/Thiyaga158)
+
+---
+
+## License
+
+This model is licensed under [CC BY-NC 3.0](https://creativecommons.org/licenses/by-nc/3.0/).  
+For research and educational use only.
+
 ---