Our project is basically for brain diagnosis for which we use MRI scans. It converts T1-weighted MRI images to T2-weighted MRI images, enabling more efficient diagnostics.
This project focuses on the development of an AI-powered solution that converts T1-weighted MRI images to T2-weighted MRI images, enabling more efficient diagnostics and reducing the need for multiple MRI scans. By leveraging deep learning techniques and a U-Net architecture, the model learns to accurately translate images from one modality (T1) to another (T2).
1. Problem Identification: Developed a model to generate T2-weighted MRI images from T1 images, reducing the need for additional scans. 2. Data Preparation: Segregated the dataset into Training (70%), Cross-validation (20%), and Testing (10%) sets. 3. Model Development: Built a 3D U-Net model with convolutional layers for T1 to T2 translation. 4. Training: Achieved MSE = 0.3 with continuous improvements during training. 5. Results: Successfully generated T2 images from T1 with promising accuracy. 6. Challenges Overcome: Handled data augmentation and preprocessing to improve model performance. 7. Next Steps: Fine-tune the model for better accuracy and prepare for real-world deployment.
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