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

Dragonscypher/rayz_EfficientNet_B0

Rayz : AI-Powered Chest X-ray Analysis

🩺 Overview