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@shrprabh shrprabh released this 02 Dec 07:09
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v1.0.0
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Release Notes for [Your Model Name: DuckNet-Based Segmentation Model]

Version: 1.0

Release Date: 2nd December 2024

Overview

This release unveils a cutting-edge segmentation model built on the enhanced DuckNet architecture (U-Net + DenseNet), developed specifically for high-precision tasks such as 3D MRI brain tumor segmentation. The model demonstrates state-of-the-art performance across key metrics, achieving 99.58% validation accuracy and a Dice Coefficient of 88.72% for validation, making it a standout solution for segmentation challenges.

Performance Metrics

Metric Model 1: 3D MRI Brain Tumor Segmentation Model 2: U-Net Model 3: U-Net + CNN (BRATS) Model 4: DuckNet (U-Net + DenseNet)
Accuracy (Train) 99.02% 99.31% 98.67% 99.25%
Accuracy (Validation) 98.91% 99.31% 98.34% 99.57%–99.58%
Mean IoU 77.16% (Train), 78.25% (Val) 84.26% N/A N/A
Dice Coefficient (Train) 48.73% 64.8% 35.89% 88.14%
Dice Coefficient (Val) 47.03% 64.8% 28.22% 88.72%
Precision 99.33% 99.35% 60.47% High
Sensitivity (Recall) 98.64% (Train), 98.56% (Val) 99.16% 63.97% Moderate to High
Specificity N/A 99.78% 98.74% High
Validation Loss N/A 0.0267 0.0592 0.0103

Key Highlights

  1. Top Performance:

    • DuckNet (U-Net + DenseNet) emerged as the best model, excelling in all key performance metrics:
      • Dice Coefficient: 88.72% (Validation), 88.14% (Training)
      • Validation Loss: 0.0103 (Lowest among models)

  2. Accurate and Reliable:

    • Achieved 99.58% validation accuracy, with high precision and specificity.
    • Demonstrated robust segmentation quality through significantly higher Dice Coefficient and Mean IoU.

  3. Dataset Utilized:

    • Trained on 9,000 images of diverse brain MRI data, ensuring generalizability and robustness.

  4. Architecture:

    • Combines U-Net and DenseNet, with modifications that remove residual blocks to optimize performance and computational efficiency.

Why DuckNet Is the Best

  • Highest Dice Coefficient: The model excels at segmenting fine details and structures, making it ideal for medical imaging tasks.
  • Lowest Validation Loss: A loss of 0.0103 indicates superior generalization on unseen data.
  • Consistent Accuracy: High validation and training accuracy across all metrics, outperforming traditional U-Net and other models.

Applications

This model can be effectively applied to:

  • Medical Imaging: Accurate tumor segmentation in MRI scans.
  • General Object Segmentation: Tasks requiring high precision in delineating boundaries.

Acknowledgments

This work is built upon the DUCK-Net architecture originally proposed by Razvan Du.

Development Team:

  • Shreyas Prabhakar
  • Suman Majjari
  • Siva Pavan Inja
  • Talha Jabbar
  • Aditya Madalla

Supervised by: Professor Victor Sheng

License

The model is released under the Creative Commons Attribution 4.0 International License (CC BY 4.0). Refer to the LICENSE file for detailed terms and conditions.

Contact

For further information, inquiries, or collaborations:
Shreyas Prabhakar
[[email protected] or [email protected]]

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