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---
language: []
license: mit
tags:
- chest-xray
- efficientnet-b0
- medical-ai
- radiology
- deep-learning
- image-classification
library_name: transformers
datasets:
- nih-chest-xray
- nlmcxr
model-index:
- name: rayz_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
---
# 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**
```bash
pip install torch torchvision transformers pillow numpy matplotlib seaborn
|