README.md

metadata

title: Pneumonia Detection System
emoji: 🩺
colorFrom: blue
colorTo: green
sdk: gradio
sdk_version: 3.1.4
app_file: app.py
pinned: false

Pneumonia Detection System

A Flask-based web application that uses a fine-tuned VGG19 model to detect pneumonia from chest X-ray images.

Model Links

• CNN Model

Setup Instructions

Prerequisites

• Python 3.8 or higher

Installation

1. Clone the repository:

git clone https://github.com/yourusername/Pneumono_Detect.git
cd Pneumono_Detect

2. Set up the environment:

Windows:

./setup.bat

Linux/Mac:

chmod +x setup.sh
./setup.sh

Activating the Environment

Windows:

tf_test_env\Scripts\activate

Linux/Mac:

source tf_test_env/bin/activate

Running the Application

1. Ensure your virtual environment is activated
2. Run the Flask application:

python app.py

3. Open a web browser and navigate to http://localhost:5000

Usage

1. Upload a chest X-ray image through the web interface
2. Click "Predict" to get the classification result
3. View the prediction result and confidence score

Project Structure

Pneumono_Detect/
├── app.py                  # Flask application
├── requirements.txt        # Python dependencies
├── setup.bat              # Windows setup script
├── setup.sh               # Linux/Mac setup script
├── static/
│   └── uploads/           # Folder for uploaded images
└── templates/
    ├── index.html         # Upload page
    └── result.html        # Results page

Model Information

• Architecture: VGG19 (fine-tuned)
• Input Size: 128x128x3
• Classes: NORMAL, PNEUMONIA
• Confidence Threshold: 0.7

Dependencies

• Flask 3.1.0
• TensorFlow 2.12.0
• Pillow 10.2.0
• NumPy 1.23.5