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LEVERAGE PAPER RESULTS SUMMARY
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Experiment Timestamp: 20251124_180934
WMH Segmentation: Binary vs Three-class Classification Comparison
DATASET INFORMATION:
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Training Images: 1044
Test Images: 161
Image Size: (256, 256)
Classes: Background (0), Normal WMH (1), Abnormal WMH (2)
METHODOLOGY:
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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:
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| Scenario 1 (Binary) | Scenario 2 (3-class) | Improvement
--------------------|---------------------|----------------------|------------
Accuracy | 0.9751 | 0.9915 | +0.0164
Precision | 0.2306 | 0.4637 | +0.2331
Recall | 0.9838 | 0.7961 | -0.1876
Dice Coefficient | 0.3736 | 0.5861 | +0.2125
IoU Coefficient | 0.2297 | 0.4145 | +0.1848
STATISTICAL SIGNIFICANCE:
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DICE COEFFICIENT:
Test: Paired t-test
t-statistic: 9.1289
p-value: 0.0000
Effect Size (Cohen's d): 0.5655
95% Confidence Interval: [0.1278, 0.1983]
Result: SIGNIFICANT improvement
IoU COEFFICIENT:
Test: Paired t-test
t-statistic: 9.2000
p-value: 0.0000
Effect Size (Cohen's d): 0.6282
95% Confidence Interval: [0.1177, 0.1821]
Result: SIGNIFICANT improvement
KEY FINDINGS:
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1. Three-class segmentation shows 72.03% improvement in Dice coefficient
2. Three-class segmentation shows 99.70% 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:
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- 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:
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✓ 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
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