Chest X-ray Detection 🫁

This project is a Chest X-ray Detection web application developed using Streamlit and YOLO. The app allows users to upload chest X-ray images and performs real-time predictions using a pre-trained YOLO model. The result includes the predicted label and the associated probability.

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Features

• Upload & Predict: Upload X-ray images in jpg, png, or jpeg formats.
• Model Predictions: Detect chest conditions with YOLO and display the prediction with confidence.
• Interactive Interface: Built using Streamlit for an intuitive and user-friendly experience.

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About the Dataset

The dataset used in this project consists of chest X-ray images prepared by combining multiple publicly available datasets. Special care was taken to ensure the dataset's integrity by removing duplicate images using a tool called VisiPics.

Classes

The dataset is organized into the following classes:

1. Normal Lungs: Images showing healthy lung conditions.
2. Diseased Lungs: Divided into four categories representing various lung diseases:
   • Bacterial Pneumonia
   • Corona Virus Disease (COVID-19)
   • Tuberculosis
   • Viral Pneumonia

Data Augmentation

To improve the dataset's balance and enhance the model's generalization capabilities, data augmentation was applied with a factor of 6. This process included:

• Rotations
• Flipping (horizontal and/or vertical)
• Contrast Adjustments

The augmentation increased the dataset size to approximately 10,000 images, ensuring sufficient variability for training deep learning models while retaining the original dataset's essence.

Dataset Structure

The dataset is divided into three subsets:

1. Training Set: Used to train the deep learning model.
2. Validation Set: Used during training to evaluate the model's performance and fine-tune hyperparameters.
3. Test Set: Used to assess the model's final performance on unseen data.

This dataset serves as a comprehensive collection for training, validating, and testing deep learning models for lung disease classification tasks.

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Installation Instructions

Follow these steps to set up the project on your local machine:

Prerequisites

Ensure you have the following installed:

• Python 3.11 or later
• pip (Python package manager)

Steps

1. Clone the Repository
   Download or clone the project repository:

   git clone https://github.com/Osama-Abo-Bakr/Chest-X-ray-Detection.git
   cd Chest-Xray-Detection

2. Create a Virtual Environment (Optional but Recommended)
   Set up a virtual environment to isolate project dependencies:

   python -m venv env
   source env/bin/activate  # On Windows: env\Scripts\activate

3. Install Dependencies
   Install the required Python packages using pip:

   pip install -r requirements.txt

4. Download the Model
   Ensure the YOLO model file (model_yolo-v1.pt) is placed in the models/ directory. If the directory or model file is missing, create the folder and download the model.

   mkdir -p models
   # Place your YOLO model file in the models directory

5. Run the Application
   Launch the Streamlit app:

   streamlit run main.py

6. Access the Application
   Open the URL shown in the terminal (usually http://localhost:8501) to access the app.

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File Structure

Chest-Xray-Detection/
│
├── main.py               # Main application file
├── models/
│   └── model_yolo-v1.pt  # YOLO model file
├── requirements.txt      # Required Python packages
└── README.md             # Project documentation

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Requirements

The project requires the following Python packages:

• Streamlit: 1.24.0
• Ultralytics: 8.3.0
• NumPy: 1.24.4
• Pillow: 9.4.0

Install all dependencies by running:

pip install -r requirements.txt

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Troubleshooting

• Image Upload Issues: Ensure the uploaded file is in one of the supported formats (jpg, png, jpeg).
• Model File Missing: Place the YOLO model file (model_yolo-v1.pt) in the models/ directory.

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Contributing

Contributions are welcome! Feel free to fork the repository and submit a pull request.

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Happy detecting! 🫁