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The model aims to assist in the early detection of preneumonia by analyzing medical imaging data.\n\n## Features\n- Utilizes EfficientNet for robust image classification\n- Implements PyTorch for model training and evaluation\n- Detects the presence of preneumonia with high training accuracy\n\n## Results\nWhile the model demonstrates high training accuracy, it exhibits lower test accuracy. This indicates potential overfitting, but it successfully detects the presence of preneumonia in the training data.\n\n## Installation\nTo get started, clone the repository and install the required dependencies:\n\nIn Docker and JupyterLab\n\n```bash\ngit clone https://github.com/lucianoscarpaci/Preneumonia-Classification.git\n\n```\n## License\nThis project is licensed under the MIT License.","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Flucianoscarpaci%2Fpreneumonia-classification","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Flucianoscarpaci%2Fpreneumonia-classification","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Flucianoscarpaci%2Fpreneumonia-classification/lists"}