Create README.md

⚠️ Bias, Risks, and Limitations
🚧 Potential Biases

Trained primarily on US-based patient data
May not generalize well to different demographics & imaging techniques
Model confidence does not equal correctness

🚑 Limitations

Not a diagnostic tool: AI cannot replace radiologists
Best suited for frontal X-rays (not lateral)
Cannot detect rare conditions not in the dataset

✅ Recommendations

Always consult a radiologist for diagnosis.
Fine-tune the model on regional hospital data if needed.
Use ensemble learning for higher accuracy.

📖 Training Details

Dataset 1: NIH Chest X-ray Dataset (~100,000 images)
Dataset 2: NLMCXR Dataset (Radiology Reports + X-ray images)
Loss Function: Cross-Entropy Loss
Optimizer: Adam with Learning Rate Scheduling
Batch Size: 32
Epochs: 20
Hardware Used: NVIDIA V100 GPU (AWS Cloud)

📊 Evaluation Metrics

AUROC (Area Under Receiver Operating Curve)
Precision / Recall
F1 Score
Accuracy across conditions

🌱 Environmental Impact

This model was trained on cloud GPUs with optimizations to reduce energy consumption.

Hardware Type: NVIDIA V100 GPU
Total Training Time: ~20 GPU Hours
Cloud Provider: AWS (US-East)
Estimated Carbon Emissions: ~15 kg CO₂

🤝 Acknowledgments

NIH & NLMCXR datasets
Hugging Face & PyTorch for model training
Community support & AI healthcare initiatives
---

### **What This Model Card Covers:**
✅ **Full project backstory** (False TB result → AI model solution)
✅ **EfficientNet_B0 vs. Other models** (Why it was chosen)
✅ **How to use it** (Code + Dependencies)
✅ **Evaluation metrics & Dataset details**
✅ **Bias, Risks, & Future Work**


Let me know if you need **further refinements** before uploading.

README.md

@@ -0,0 +1,67 @@
+---
+language: []
+license: mit
+tags:
+  - chest-xray
+  - efficientnet-b0
+  - medical-ai
+  - radiology
+  - deep-learning
+datasets:
+  - nih-chest-xray
+  - nlmcxr
+model-index:
+  - name: AI-Powered Chest X-ray Analysis (EfficientNet_B0)
+    results:
+      - task:
+          type: image-classification
+        dataset:
+          name: nih-chest-xray
+          type: medical-image
+        metrics:
+          - name: AUROC Score
+            type: accuracy
+            value: 0.72 - 0.93
+---
+
+# AI-Powered Chest X-ray Analysis (EfficientNet_B0)
+
+## 🩺 Overview
+
+This model analyzes **chest X-rays** to detect **14 potential lung conditions** using **EfficientNet_B0**, a lightweight yet high-performing CNN. It was trained on **NIH Chest X-ray Dataset & NLMCXR Dataset**, providing reliable multi-class classification for various lung diseases.
+
+### 🚀 Motivation
+This project began when I received a **false-positive tuberculosis (TB) report** and had to wait for **delayed X-ray results** due to a holiday. Not knowing how to interpret X-rays, I **built this AI tool** to **help others in similar situations**.
+
+## 📜 Model Details
+
+- **Model type**: Image Classification (Chest X-ray Analysis)
+- **Architecture**: EfficientNet_B0
+- **Trained on**: NIH Chest X-ray & NLMCXR Datasets
+- **Input format**: Chest X-ray images (`.png`, `.jpg`)
+- **Output**: Probabilities for 14 lung conditions
+- **License**: MIT
+- **Compute Requirement**: Can run on CPU, optimized for **GPU (CUDA)**
+
+## 💡 Why EfficientNet_B0?
+I tested multiple models, including **DenseNet121, ViT, and CNNs**, but **EfficientNet_B0_best_93.44** outperformed the others in terms of:
+- **High Accuracy (AUROC: 0.72 - 0.93)**
+- **Lower Computational Cost**
+- **Faster Inference Speed**
+- **Better Generalization across datasets**
+
+## 📊 Model Performance
+
+| Model              | AUROC Score (Avg) |
+|--------------------|------------------|
+| **EfficientNet_B0** | **0.72 - 0.93** |
+| DenseNet121       | 0.55 - 0.95      |
+| ViT_Base          | 0.32 - 0.65      |
+
+---
+
+## 🔧 How to Use the Model
+
+### **1️⃣ Install Dependencies**
+```bash
+pip install torch torchvision transformers pillow numpy matplotlib seaborn