Update README.md

README.md

@@ -1,3 +1,53 @@
----
-license: apache-2.0
----
+---
+license: mit
+tags:
+  - medical-imaging
+  - chest-xray
+  - pneumonia-detection
+  - efficientnet
+  - pytorch
+  - image-classification
+datasets:
+  - chest-xray-pneumonia
+metrics:
+  - accuracy
+  - auc
+---
+
+# MediScan AI — EfficientNetB4 Chest X-Ray Classifier
+
+Classifies chest X-rays as **NORMAL** or **PNEUMONIA**.
+
+## Model Details
+- Architecture: EfficientNetB4 (transfer learning, two-phase fine-tuning)
+- Input: 380×380 RGB chest X-ray image
+- Output: NORMAL | PNEUMONIA + confidence score
+- Explainability: Grad-CAM heatmap overlay
+
+## Performance (Kaggle Chest X-Ray Test Set, n=624)
+| Metric | Value |
+|--------|-------|
+| Accuracy | 87.66% |
+| AUC-ROC | 0.9428 |
+| Avg Precision | 0.9605 |
+| Pneumonia Recall | 93.59% |
+
+## Training
+- Dataset: Kaggle Chest X-Ray Images (Pneumonia) — 5,863 images
+- Optimizer: AdamW + Cosine Annealing
+- Epochs: 7 (early stopping)
+- Hardware: Kaggle T4 GPU (8.5 min)
+
+## Usage
+```python
+import torch
+from inference import engine
+
+engine.load("mediscan_v5.pth")
+result = engine.predict(open("xray.jpg", "rb").read())
+print(result["predicted_class"], result["confidence"])
+```
+
+## Disclaimer
+For research and educational purposes only.
+Not a certified medical device.