Create README.md

README.md

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+---
+language: en
+tags:
+- deep-learning
+- computer-vision
+- medical-imaging
+- chest-xray
+- pneumonia
+- keras
+- tensorflow
+license: cc-by-4.0
+datasets:
+- chest-xray-pneumonia
+widget:
+- text: Upload a chest X-ray image (.jpg or .png)
+---
+
+# 🩻 Pneumonia Detection Model (CNN)
+
+## 🧠 Overview
+This model is a **Convolutional Neural Network (CNN)** trained to classify **chest X-ray images** as either **Normal** or **Pneumonia**.  
+It demonstrates the application of **deep learning** to **medical imaging classification** and serves as an **educational and research tool** — **not** for clinical use.
+
+---
+
+## ⚙️ Model Details
+
+- **Architecture:** Custom CNN (3 convolutional + 2 dense layers)  
+- **Framework:** TensorFlow / Keras  
+- **Input size:** 150×150 RGB images  
+- **Output:** Binary classification — `0 = Normal`, `1 = Pneumonia`  
+- **Dataset:** *Chest X-Ray Images (Pneumonia)* — [Kermany et al., *Cell* (2018)](http://www.cell.com/cell/fulltext/S0092-8674(18)30154-5)
+
+---
+
+## 📚 Dataset Information
+
+**Source:**  
+Kermany, D.S., Goldbaum, M., Cai, W. *et al.*  
+*Identifying Medical Diagnoses and Treatable Diseases by Image-Based Deep Learning*,  
+*Cell* (2018), 172(5), 1122–1131.e9  
+[DOI: 10.1016/j.cell.2018.02.010](https://doi.org/10.1016/j.cell.2018.02.010)
+
+**License:** [Creative Commons Attribution 4.0 International (CC BY 4.0)](https://creativecommons.org/licenses/by/4.0/)
+
+---
+
+## 🩺 Intended Use
+
+This model is designed for:
+- Machine learning and data science education  
+- Research in medical image analysis  
+- Prototyping of AI healthcare applications  
+
+⚠️ **Disclaimer:**  
+This model is **not a medical device** and **should not be used for clinical or diagnostic purposes**.  
+It is provided **solely for educational and research use**.
+
+---
+
+## 🧩 How It Works
+
+1. The user uploads a **chest X-ray image** (`.jpg` or `.png`).
+2. The image is resized to **150×150** pixels and normalized to `[0, 1]`.
+3. The CNN processes the image and outputs a probability:
+   - `0 → Normal`
+   - `1 → Pneumonia`
+4. The final classification is the class with the highest confidence score.
+
+---
+
+## 📊 Evaluation Report
+
+### 🧾 Classification Metrics
+| Metric | Normal | Pneumonia |
+|:--------|:--------:|:-----------:|
+| **Precision** | 0.962 | 0.829 |
+| **Recall** | 0.659 | 0.985 |
+| **F1-score** | 0.783 | 0.900 |
+| **Support** | 232 | 389 |
+
+**Overall accuracy:** 0.863  
+**Macro F1-score:** 0.841  
+**Weighted F1-score:** 0.856  
+
+---
+
+### 📉 ROC Curve  
+![ROC Curve](https://huggingface.co/dlopez350/pneumonia_detector/resolve/main/roc_curve.png)
+
+**AUC = 0.959**
+
+---
+
+### 📊 Confusion Matrix  
+![Confusion Matrix](https://huggingface.co/dlopez350/pneumonia_detector/resolve/main/confusion_matrix.png)
+
+| True Label | Predicted Normal | Predicted Pneumonia |
+|-------------|------------------|---------------------|
+| **Normal** | 153 | 79 |
+| **Pneumonia** | 6 | 383 |
+
+---
+
+## 💡 Example Usage
+
+```python
+import tensorflow as tf
+import numpy as np
+from tensorflow.keras.preprocessing import image
+
+# Load model
+model = tf.keras.models.load_model("cnn_pneumonia.keras")
+
+# Load and preprocess image
+img_path = "test_image.jpg"
+img = image.load_img(img_path, target_size=(150, 150))
+img_array = image.img_to_array(img) / 255.0
+img_array = np.expand_dims(img_array, axis=0)
+
+# Predict
+prediction = model.predict(img_array)[0][0]
+label = "Pneumonia" if prediction > 0.5 else "Normal"
+confidence = prediction if prediction > 0.5 else 1 - prediction
+
+print(f"Prediction: {label} ({confidence:.2f} confidence)")
+```