Rayz : AI-Powered Chest X-ray Analysis
🩺 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
pip install torch torchvision transformers pillow numpy matplotlib seaborn
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