This project focuses on the segmentation of brain tumors using the Brain Tumor Segmentation (BRATs) dataset. The primary goal was to develop a deep learning model capable of accurately identifying and segmenting tumor regions in MRI scans.
- Advanced Data Preprocessing: Cleaned and preprocessed MRI scans to ensure high-quality input data.
- Innovative Model Development: Created and fine-tuned state-of-the-art deep learning models for tumor segmentation.
- Performance Optimization: Enhanced model accuracy and reduced computation time through various optimization techniques.
- Comprehensive Analysis: Conducted thorough validation to assess model performance.
- Loaded and normalized the MRI scans.
- Augmented the dataset to improve model robustness.
- Split the data into training, validation, and test sets.
Here are some examples of preprocessed MRI scans:
- Used a U-Net architecture for segmentation.
- Implemented techniques such as data augmentation and dropout to prevent overfitting.
- Trained the model using cross-entropy loss and the Adam optimizer.
- Accuracy: Achieved an accuracy of 95%.
- Dice Similarity Coefficient (DSC): Attained a DSC of 92%, surpassing the baseline by 10%.
- Inference Time Reduction: Reduced inference time by 30%.
The graph below shows the training and validation loss over epochs:
The graph below shows the training and validation accuracy over epochs:
Here are some sample outputs from the segmentation model:
- Python 3.7+
- Jupyter Notebook
- Required libraries:
numpy,pandas,tensorflow,keras,sklearn,matplotlib
Clone the repository:
git https://github.com/billu2002/Brain-Segmentation.git