{"id":23722431,"url":"https://github.com/repo-bilalnaeem/brain-segmentation","last_synced_at":"2026-02-14T22:30:14.003Z","repository":{"id":240488506,"uuid":"789456481","full_name":"repo-bilalnaeem/Brain-Segmentation","owner":"repo-bilalnaeem","description":"This project focuses on the segmentation of brain tumors using the Brain Tumor Segmentation (BRATs) dataset. 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The primary goal was to develop a deep learning model capable of accurately identifying and segmenting tumor regions in MRI scans.\n\n## Project Highlights\n- **Advanced Data Preprocessing**: Cleaned and preprocessed MRI scans to ensure high-quality input data.\n- **Innovative Model Development**: Created and fine-tuned state-of-the-art deep learning models for tumor segmentation.\n- **Performance Optimization**: Enhanced model accuracy and reduced computation time through various optimization techniques.\n- **Comprehensive Analysis**: Conducted thorough validation to assess model performance.\n\n## Methodology\n### Data Preprocessing\n- Loaded and normalized the MRI scans.\n- Augmented the dataset to improve model robustness.\n- Split the data into training, validation, and test sets.\n\n#### Sample Preprocessed Images\nHere are some examples of preprocessed MRI scans:\n\n![Preprocessed Image 1](Pre-processed-output-1.png)\n![Preprocessed Image 2](Pre-processed-output-2.png)\n\n### Model Development\n- Used a U-Net architecture for segmentation.\n- Implemented techniques such as data augmentation and dropout to prevent overfitting.\n- Trained the model using cross-entropy loss and the Adam optimizer.\n\n### Performance Metrics\n- **Accuracy**: Achieved an accuracy of 95%.\n- **Dice Similarity Coefficient (DSC)**: Attained a DSC of 92%, surpassing the baseline by 10%.\n- **Inference Time Reduction**: Reduced inference time by 30%.\n\n## Results\n\n### Training and Validation Loss\nThe graph below shows the training and validation loss over epochs:\n\n![Training and Validation Loss](Training-Validation-Loss.png)\n\n### Training and Validation Accuracy\nThe graph below shows the training and validation accuracy over epochs:\n\n![Training and Validation Accuracy](Tainining-Validation-Accuracy.png)\n\n### Sample Predictions\nHere are some sample outputs from the segmentation model:\n\n![Prediction Image 1](Predicted-Outputs.png)\n\n## Usage\n### Prerequisites\n- Python 3.7+\n- Jupyter Notebook\n- Required libraries: `numpy`, `pandas`, `tensorflow`, `keras`, `sklearn`, `matplotlib`\n\n### Installation\nClone the repository:\n```bash\ngit https://github.com/billu2002/Brain-Segmentation.git\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Frepo-bilalnaeem%2Fbrain-segmentation","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Frepo-bilalnaeem%2Fbrain-segmentation","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Frepo-bilalnaeem%2Fbrain-segmentation/lists"}