Organ Classification AI Model

This project focuses on creating a deep learning-based AI model to classify medical images of organs into one of four categories: Eye, Breast, Brain, and Limbs. The model uses TensorFlow and is paired with a user-friendly PyQt5-based GUI to allow seamless interaction.

Features

• Model Architecture: A custom CNN built using TensorFlow to classify images into four organ categories.
• User Interface: A dynamic GUI created with PyQt5 to:
  • Upload an image for classification.
  • Display the predicted organ class with confidence levels.
  • Provide notifications for incorrect inputs.
• Training Pipeline: Organized code structure for training the model on your dataset.
• Dataset Management: Structured dataset folder for training, validation, and testing.

Project Structure

organ-classifier/
├── app/
│   ├── src/
│   │   ├── main.py                  # Main entry point for the app, runs the PyQt5 GUI
│   │   ├── model.py                 # Contains the model architecture and loading code
│   │   ├── dataset_utils.py         # Functions related to loading or processing the dataset
│   │   ├── predict.py               # Functionality to handle predictions and model inference
│   ├── assets/                      # Images, icons, and other UI assets
│   │   └── logo.png                 # Example logo for the UI (optional)
│   ├── saved_models/                # Folder to store saved models (e.g., trained model weights)
│   │   └── trained_model.h5         # Trained TensorFlow model file
│   ├── requirements.txt             # List of dependencies needed to run the project
│   └── README.md                    # Project documentation (this file)
└── dataset/                         # Dataset folder containing images for training and testing
    ├── train/                       # Training data
    │   ├── Eye/
    │   ├── Breast/
    │   ├── Brain/
    │   └── Limbs/
    ├── validate/                    # Validation data
    │   ├── Eye/
    │   ├── Breast/
    │   ├── Brain/
    │   └── Limbs/
    └── test/                        # Test data
        ├── Eye/
        ├── Breast/
        ├── Brain/
        └── Limbs/

Installation

1. Clone the repository:

   git clone https://github.com/yourusername/organ-classifier.git
   cd organ-classifier

2. Install the required dependencies:

   pip install -r app/requirements.txt

3. Ensure you have Python 3.8 or later installed.

Usage

Training the Model

1. Place your dataset into the dataset/ folder, ensuring it follows the structure outlined above.
2. Modify any hyperparameters or paths in train.py as needed.
3. Run the training script:

   python app/src/train.py

Running the Application

1. Ensure the trained model (trained_model.h5) is in the saved_models/ directory.
2. Launch the GUI:

   python app/src/main.py

3. Use the GUI to upload images and view predictions.

Dataset Preparation

• The dataset should be organized into train, validate, and test directories.
• Each category (Eye, Breast, Brain, Limbs) should have its own subdirectory containing images.
• Example:

  dataset/
  ├── train/
  │   ├── Eye/
  │   │   ├── image1.jpg
  │   │   └── image2.jpg
  │   ├── Breast/
  │   └── ...
  └── test/
      ├── Eye/
      └── ...
  

Notes

• Ensure all images are preprocessed to the required input size for the model (default is 224x224).
• The model's output includes the predicted class and the associated confidence score.

Future Improvements

• Incorporate additional organ classes.
• Enhance the GUI for better user experience.
• Add real-time image preprocessing and augmentation.

License

This project is licensed under the MIT License. See the LICENSE file for details.

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Feel free to contribute to the project by submitting pull requests or reporting issues!