README.md

---
license: mit
language: en
library_name: tensorflow
tags:
  - medical-imaging
  - chest-xray
  - pneumonia-detection
  - pediatric
  - computer-vision
  - cross-validation
datasets:
  - paultimothymooney/chest-xray-pneumonia
  - iamtanmayshukla/pneumonia-radiography-dataset
metrics:
  - accuracy
  - sensitivity
  - specificity
model-index:
- name: PneumoDetectAI
  results:
  - task:
      type: image-classification
      name: Pediatric Pneumonia Detection
    dataset:
      name: Cross-Operator Validation Dataset
      type: medical-imaging
    metrics:
      - type: accuracy
        name: Cross-Operator Accuracy
        value: 0.86
      - type: sensitivity
        name: Sensitivity
        value: 0.964
      - type: specificity
        name: Specificity
        value: 0.748
---

# PneumoDetectAI

Binary classification model for pneumonia detection in pediatric chest X-rays (ages 1-5). Built with TensorFlow and MobileNetV2, validated on independent operator cohort with 86% accuracy and 96.4% sensitivity.

**Author**: Ayushi Rathour  
**Contact**: [email protected]  
**Framework**: TensorFlow 2.19  
**Model Size**: ~14 MB  

## Model Overview

PneumoDetectAI is a deep learning model designed to detect pneumonia in chest X-rays of pediatric patients aged 1 to 5 years. The model uses transfer learning from MobileNetV2 for efficient inference while maintaining clinically relevant performance.

### Key Specifications

| Property | Value |
|----------|-------|
| **Architecture** | MobileNetV2 (ImageNet pretrained) + custom head |
| **Input Shape** | 224 × 224 × 3 (RGB) |
| **Output** | Binary classification (NORMAL/PNEUMONIA) |
| **File Format** | TensorFlow SavedModel (.h5) |
| **Model Size** | ~14 MB |
| **Inference Time** | 0.46 seconds on CPU |
| **Target Population** | Pediatric patients (1-5 years) |

### Intended Users
- ML researchers working on medical imaging
- Healthcare AI developers building screening tools
- Students learning medical AI validation approaches
- Radiologists interested in AI-assisted screening

## Performance Metrics

| Validation Type | Dataset | Samples | Accuracy | Sensitivity | Specificity |
|-----------------|---------|---------|----------|-------------|-------------|
| **Internal** | Mooney 2018 | 269 | 94.8% | 89.6% | 100% |
| **Cross-Operator** | Radiography 2024 | 485 | **86.0%** | **96.4%** | 74.8% |

### Clinical Interpretation
- **High Sensitivity (96.4%)**: Catches 96 out of 100 pneumonia cases, suitable for screening
- **Moderate Specificity (74.8%)**: 25% false positive rate acceptable for screening tool
- **Generalization**: 8.8% accuracy drop on independent cohort indicates reasonable robustness

## Quick Start Usage

```python
from huggingface_hub import hf_hub_download
import tensorflow as tf
import numpy as np
from PIL import Image

# Download and load model
model_path = hf_hub_download(
    repo_id="ayushirathour/chest-xray-pneumonia-detection",
    filename="best_chest_xray_model.h5"
)
model = tf.keras.models.load_model(model_path)

# Preprocess image
def preprocess_xray(image_path):
    img = Image.open(image_path).convert("RGB").resize((224, 224))
    img_array = np.array(img) / 255.0
    return np.expand_dims(img_array, axis=0)

# Make prediction
image_array = preprocess_xray("chest_xray.jpg")
probability = model.predict(image_array)[0][0]
diagnosis = "PNEUMONIA" if probability >= 0.5 else "NORMAL"
confidence = probability * 100 if probability >= 0.5 else (1 - probability) * 100

print(f"Diagnosis: {diagnosis}")
print(f"Confidence: {confidence:.1f}%")
```

## Training Details

### Datasets
- **Training Data**: Chest X-Ray Images (Pneumonia) by Paul Timothy Mooney
  - Source: Guangzhou Women and Children's Medical Center
  - Size: ~5,863 images (pediatric patients aged 1-5)
  - Split: Pre-divided train/validation/test

- **External Validation**: Pneumonia Radiography Dataset by Tanmay Shukla
  - Source: Same hospital, different operators and time period
  - Size: 485 independent samples
  - Purpose: Cross-operator generalization testing

### Architecture Details
- **Base Model**: MobileNetV2 (ImageNet weights frozen initially)
- **Custom Head**: Global Average Pooling → Dropout (0.5) → Dense (128) → Dense (1, sigmoid)
- **Optimization**: Adam optimizer (lr=0.0001)
- **Loss Function**: Binary crossentropy
- **Training**: 20 epochs with early stopping

## Limitations & Risks

### Technical Limitations
- **Single Institution**: Both datasets from same medical center
- **Age Restriction**: Validated only on pediatric patients (1-5 years)
- **Binary Output**: Cannot distinguish pneumonia subtypes (viral vs bacterial)
- **Image Quality**: Performance degrades with poor quality or non-standard views

### Clinical Limitations
- **False Positive Rate**: 25.2% may increase radiologist workload
- **Screening Only**: Not suitable for definitive diagnosis
- **Population Bias**: Trained on Asian pediatric cohort only
- **No Clinical Context**: Cannot incorporate patient history or symptoms

### Deployment Risks
- **Overconfidence**: High sensitivity may create false sense of security
- **Misuse**: Risk of use without proper medical oversight
- **Generalization**: Performance may vary on different imaging equipment

## Responsible AI & Ethics

### Bias Considerations
- **Population Bias**: Model trained exclusively on Asian pediatric population
- **Institutional Bias**: Single medical center may not represent global imaging practices
- **Age Bias**: Performance on other age groups unknown

### Required Safeguards
- **Human Oversight**: All predictions must be reviewed by qualified radiologists
- **Screening Context**: Should only be used as preliminary screening tool
- **Informed Consent**: Patients must be informed of AI involvement in screening
- **Quality Assurance**: Regular monitoring of real-world performance required

### Regulatory Status
- **Not FDA Approved**: Research prototype only
- **Not CE Marked**: Not approved for clinical use in EU
- **Research Use**: Intended for academic and development purposes only

## Citation

```bibtex
@misc{rathour2025pneumodetectai,
    title={PneumoDetectAI: Pediatric Chest X-Ray Pneumonia Detection with Cross-Operator Validation},
    author={Rathour, Ayushi},
    year={2025},
    note={Cross-operator validation on 485 independent samples},
    url={https://huggingface.co/ayushirathour/chest-xray-pneumonia-detection}
}
```

## Acknowledgements

### Datasets
- **Training Dataset**: Chest X-Ray Images (Pneumonia) - Paul Timothy Mooney (Kaggle)
- **Validation Dataset**: Pneumonia Radiography Dataset - Tanmay Shukla (Kaggle)
- **Original Research**: Kermany et al., "Identifying Medical Diagnoses and Treatable Diseases by Image-Based Deep Learning", Cell 2018

### Technical Stack
- **Framework**: TensorFlow 2.19
- **Architecture**: MobileNetV2 (Google)
- **Deployment**: Streamlit, FastAPI
- **Hosting**: Hugging Face Hub

## Additional Resources

- 🌐 **Live Demo**: [PneumoDetectAI Web App](https://pneumodetectai.streamlit.app/)
- 📂 **Source Code**: [GitHub Repository](https://github.com/ayushirathour/chest-xray-pneumonia-detection-ai)
- 📖 **API Documentation**: Available when running locally
- 💬 **Issues & Support**: GitHub Issues or email contact

---

**Disclaimer**: This model is for research and educational purposes only. It is not a medical device and should not be used for clinical diagnosis without appropriate medical supervision.