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leverage_summary.txt

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-    LEVERAGE PAPER RESULTS SUMMARY
-    ================================
-    Experiment Timestamp: 20251124_152044
-    WMH Segmentation: Binary vs Three-class Classification Comparison
-
-    DATASET INFORMATION:
-    --------------------
-    Training Images: 2050 
-    Test Images: 350
-    Image Size: (256, 256)
-    Classes: Background (0), Normal WMH (1), Abnormal WMH (2)
-
-    METHODOLOGY:
-    ------------
-    Architecture: Enhanced U-Net with Batch Normalization and Dropout
-    Loss Functions: 
-    - Scenario 1: weighted_bce
-    - Scenario 2: weighted_categorical
-    Training Epochs: 50
-    Batch Size: 8
-    Learning Rate: 0.0001
-
-    PERFORMANCE RESULTS:
-    --------------------
-                        | Scenario 1 (Binary) | Scenario 2 (3-class) | Improvement
-    --------------------|---------------------|----------------------|------------
-    Accuracy            | 0.9852            | 0.9965             | +0.0112
-    Precision           | 0.3340           | 0.7589            | +0.4248
-    Recall              | 0.9682              | 0.7765               | -0.1917
-    Dice Coefficient    | 0.4967                | 0.7676                 | +0.2709
-    IoU Coefficient     | 0.3304                 | 0.6228                  | +0.2924
-
-    STATISTICAL SIGNIFICANCE:
-    -------------------------
-    DICE COEFFICIENT:
-    Test: Paired t-test
-    t-statistic: 9.6244
-    p-value: 0.0000
-    Effect Size (Cohen's d): 0.5643
-    95% Confidence Interval: [0.1353, 0.2051]
-    Result: SIGNIFICANT improvement
-
-    IoU COEFFICIENT:
-    Test: Paired t-test
-    t-statistic: 10.1596
-    p-value: 0.0000
-    Effect Size (Cohen's d): 0.6481
-    95% Confidence Interval: [0.1356, 0.2010]
-    Result: SIGNIFICANT improvement
-
-    KEY FINDINGS:
-    -------------
-    1. Three-class segmentation shows 56.54% improvement in Dice coefficient
-    2. Three-class segmentation shows 81.33% improvement in IoU coefficient
-    3. Dice analysis confirms significant improvement
-    4. IoU analysis confirms significant improvement
-    5. Post-processing provided substantial improvements in both scenarios
-
-    FILES GENERATED:
-    ----------------
-    - Models: scenario1_binary_model.h5, scenario2_multiclass_model.h5
-    - Figures: training_curves.png/.pdf, comparison_visualization.png/.pdf, metrics_comparison.png/.pdf
-    - Tables: comprehensive_results.csv/.xlsx, latex_table.tex
-    - Statistics: statistical_analysis.json, statistical_report.txt
-    - Predictions: All test predictions and ground truth data saved
-
-    PUBLICATION READINESS:
-    ----------------------
-    ✓ High-resolution figures (300 DPI, PNG/PDF)
-    ✓ LaTeX-formatted tables
-    ✓ Comprehensive statistical analysis (Dice + IoU)
-    ✓ Post-processing impact analysis
-    ✓ Reproducible results with saved models
-    ✓ Professional documentation
-