Segmentation and classification of brain tumor using 3D-unet DNN

Developed an advanced deep learning model to automate the segmentation and classification of brain tumors from 3D MRI scans, leveraging the 3D U-Net architecture. This project aims to enhance diagnostic precision and facilitate better treatment planning by providing detailed and accurate tumor delineation.

Key Components:Data Preparation and Preprocessing:Data Sourcing: Utilized comprehensive datasets, including BRATS (Brain Tumor Segmentation Challenge), to ensure diverse and representative MRI brain scans.Preprocessing Steps: Applied normalization to standardize image intensities, resampled the scans to a uniform resolution, and employed data augmentation techniques to enrich the dataset and improve model generalization.

Model Architecture:3D U-Net Design: Implemented the 3D U-Net model, characterized by an encoder-decoder structure with skip connections. The encoder captures hierarchical features at multiple scales, while the decoder reconstructs the spatial dimensions to precisely segment the tumor regions.

Custom Modifications: Tailored the architecture to address specific challenges in brain tumor imaging, such as varying tumor sizes and shapes.Training and Optimization:Training Strategy: Utilized state-of-the-art techniques for training the model, including a combination of Dice loss and cross-entropy loss to handle class imbalance and improve segmentation accuracy.

Optimization: Employed advanced optimization algorithms such as Adam to adjust the model parameters and enhance performance.Validation and Hyperparameter Tuning: Conducted rigorous validation using a separate dataset to fine-tune hyperparameters and ensure the model's robustness and generalizability.Performance Evaluation:Metrics: Assessed model performance using critical evaluation metrics such as Dice coefficient, Intersection over Union (IoU), and accuracy to gauge segmentation quality and classification effectiveness.

Result Analysis: Analyzed and visualized the segmented tumor regions to verify alignment with ground truth and assess clinical relevance.Post-Processing Enhancements:Refinement Techniques: Applied post-processing methods, including morphological operations, to enhance the clarity of segmented regions, reduce noise, and remove artifacts.

Impact: This project represents a significant advancement in the application of deep learning to medical imaging, aiming to streamline the diagnostic workflow and provide healthcare professionals with accurate and reliable tools for tumor analysis. The successful implementation of the 3D U-Net model not only improves tumor detection but also supports personalized treatment strategies by offering detailed tumor characterization.