From: Using autoencoders as a weight initialization method on deep neural networks for disease detection
Top Layers (AEs) | Accuracy (%) | MCC | Precision (%) | Recall (%) | F1 score | |
---|---|---|---|---|---|---|
Train | M: Encoding Layers | 91.21% ±1.56% | 0.81 ±0.03 | 86.61% ±3.77% | 90.84% ±2.91% | 88.59% ±1.86% |
M: Complete Autoencoder | 90.19% ±2.08% | 0.80 ±0.04 | 85.29% ±4.69% | 89.69% ±2.83% | 87.33% ±2.33% | |
WBC: Encoding Layers | 98.69% ±1.38% | 0.97 ±0.03 | 99.37% ±1.98% | 97.14% ±3.69% | 98.20% ±1.91% | |
WBC: Complete Autoencoder | 97.90% ±2.07% | 0.96 ±0.04 | 95.54% ±5.18% | 99.29% ±2.26% | 97.28% ±2.65% | |
Test | M: Encoding Layers | 89.64% | 0.78 | 90.02% | 81.40% | 85.49% |
M: Complete Autoencoder | 86.10% | 0.70 | 82.86% | 79.34% | 81.06% | |
WBC: Encoding Layers | 97.34% | 0.95 | 99.99% | 92.86% | 96.30% | |
WBC: Complete Autoencoder | 95.74% | 0.91 | 98.44% | 90.00% | 94.03% |