Table 7.
Performance comparison when adding a decoder layer with random weights when using Strategy 1 (importing only the enconder part of AE), for each of the 3 AEs — Basic AE, Denoising AE and Sparse AE — for breast cancer detection, with RNA-Seq input
| Top Layers (AEs) | Accuracy (%) | MCC | Precision (%) | Recall (%) | F1 score | |
|---|---|---|---|---|---|---|
| Approach A | AE: Encoding Layer (n =2) | 88.40 ±5.52 | 0.59 ±0.17 | 68.39 ±19.13 | 64.80 ±10.84 | 65.91 ±13.72 |
| AE: Complete Autoencoder | 91.77 ±3.13 | 0.69 ±0.12 | 80.57 ±11.79 | 67.00 ±11.24 | 72.91 ±10.86 | |
| AE: Encoding Layer (n =3) | 92.53 ±2.25 | 0.72 ±0.09 | 80.75 ±7.45 | 72.31 ±11.29 | 76.50 ±8.12 | |
| DAE: Encoding Layer (n =2) | 83.53 ±1.74 | 0.25 ±0.14 | 51.39 ±25.04 | 25.60 ±15.57 | 31.23 ±17.51 | |
| DAE: Complete Autoencoder | 87.30 ±1.90 | 0.53 ±0.05 | 63.43 ±7.13 | 58.60 ±5.17 | 60.67 ±4.58 | |
| DAE: Encoding Layer (n =3) | 87.47 ±2.81 | 0.57 ±0.08 | 62.88 ±10.52 | 68.00 ±8.99 | 64.51 ±6.24 | |
| SAE: Encoding Layer (n =2) | 79.73 ±3.86 | 0.02 ±0.05 | 9.80 ±12.48 | 3.00 ±3.16 | 4.11 ±4.09 | |
| SAE: Complete Autoencoder | 84.07 ±2.40 | 0.41 ±0.07 | 53.13 ±8.05 | 47.80 ±4.85 | 50.13 ±5.62 | |
| SAE: Encoding Layer (n =3) | 76.33 ±8.91 | 0.36 ±0.11 | 41.26 ±12.14 | 62.20 ±12.80 | 47.83 ±8.30 | |
| Approach B | AE: Encoding Layer (n =2) | 99.33 ±0.52 | 0.98 ±0.02 | 97.85 ±2.32 | 98.20 ±1.48 | 98.01 ±1.55 |
| AE: Complete Autoencoder | 99.30 ±0.37 | 0.98 ±0.01 | 99.00 ±1.06 | 96.80 ±2.35 | 97.87 ±1.15 | |
| AE: Encoding Layer (n =3) | 99.17 ±0.53 | 0.97 ±0.02 | 98.43 ±1.98 | 96.60 ±3.27 | 97.46 ±1.65 | |
| DAE: Encoding Layer (n =2) | 99.20 ±0.65 | 0.97 ±0.02 | 97.83 ±2.54 | 97.40 ±1.90 | 97.60 ±1.95 | |
| DAE: Complete Autoencoder | 99.23 ±0.52 | 0.97 ±0.02 | 98.60 ±2.08 | 96.80 ±1.69 | 97.68 ±1.57 | |
| DAE: Encoding Layer (n =3) | 99.33 ±0.38 | 0.98 ±0.01 | 99.20 ±1.40 | 96.80 ±1.69 | 98.02 ±1.08 | |
| SAE: Encoding Layer (n =2) | 96.70 ±1.24 | 0.89 ±0.05 | 95.29 ±4.78 | 84.60 ±6.47 | 89.45 ±4.14 | |
| SAE: Complete Autoencoder | 97.40 ±1.12 | 0.90 ±0.04 | 95.78 ±4.02 | 88.40 ±4.79 | 91.87 ±3.52 | |
| SAE: Encoding Layer (n =3) | 97.27 ±0.64 | 0.90 ±0.02 | 93.58 ±1.91 | 89.80 ±3.71 | 91.61 ±2.09 |
The first row presents the results for Approach A, where we fix the resulting weights of the AE pre-training; the second one shows the results for Approach B, where we allow the subsequent fine-tuning of all the weights of the model. All the presented results are the 10-fold cross-validation mean values, at the validation set, by selecting the best performing model according to its F1 score. n represents the number of layers of the encoder