Multi-Label Chest X-Ray Classifier

This project implements a robust pipeline for training a multi-label image classification model on chest X-ray data, using deep learning and transfer learning techniques. The model predicts the presence of multiple conditions in a single X-ray image.

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Table of Contents

• Features
• Technologies Used
• Project Structure
• Dataset
• Installation
• Usage
• Model Training and Evaluation
• Results
• Future Improvements

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Features

• Handles multi-label classification tasks for medical images.
• Utilizes the EfficientNetB0 architecture with transfer learning.
• Provides detailed evaluation metrics including classification report, Hamming loss, and Jaccard scores.
• Implements advanced callbacks for training optimization:
  • ReduceLROnPlateau
  • EarlyStopping
• Extensive error handling and logging for image loading and preprocessing.

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Technologies Used

• Python
• TensorFlow/Keras for deep learning model implementation.
• Pandas for data manipulation.
• NumPy for numerical computations.
• scikit-learn for data preprocessing and evaluation metrics.
• EfficientNetB0 as the base model for transfer learning.

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Project Structure

.
|-- script.py                # Main implementation file
|-- merged_CXLSeg_data.csv   # Input dataset (CSV format)
|-- images/                  # Directory containing chest X-ray images
|-- README.md                # Documentation
|-- requirements.txt         # Python dependencies

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Dataset

The project uses a dataset containing chest X-ray images annotated with multiple conditions. Each X-ray image is associated with one or more labels indicating the medical conditions present. The dataset should:

• Contain a CSV file with image paths and labels.
• Include a column named DicomPath_y specifying the file paths to the X-ray images.

Example CSV Format:

DicomPath_y	Atelectasis	Cardiomegaly	Pneumonia	...
path/to/image1.dcm	1	0	0	...
path/to/image2.dcm	0	1	1	...

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Installation

1. Clone the repository:

   git clone https://github.com/your-repo/multi-label-xray-classifier.git
   cd multi-label-xray-classifier

2. Install required dependencies:

   pip install -r requirements.txt

3. Ensure the dataset is available in the correct structure.

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Usage

1. Initialize the classifier:

   from script import ConsistentMultiLabelXRayClassifier
   
   classifier = ConsistentMultiLabelXRayClassifier(data_path='merged_CXLSeg_data.csv')

2. Train and evaluate the model:

   model, history, mlb = classifier.train_and_evaluate(epochs=15)

3. View evaluation metrics in the console output.

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Model Training and Evaluation

Training

The training pipeline:

• Loads and preprocesses images.
• Uses transfer learning with the EfficientNetB0 model.
• Fine-tunes the model with a fully connected head for multi-label classification.

Evaluation

The evaluation includes:

• Classification report with precision, recall, and F1-score for each label.
• Hamming loss.
• Jaccard scores (micro and macro averages).

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Results

• Accuracy: Achieved XX% on validation data.
• Precision/Recall/F1: Comprehensive metrics for each condition.
• Hamming Loss: YY%.
• Jaccard Score: ZZ (micro), AA (macro).

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Future Improvements

• Add support for additional datasets.
• Implement data augmentation to improve model generalization.
• Experiment with other architectures like EfficientNetV2 or Vision Transformers.
• Optimize preprocessing for large datasets using parallelization.

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License

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

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Acknowledgments

• TensorFlow/Keras team for providing pre-trained EfficientNet models.
• Dataset contributors for their invaluable work in medical imaging.

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