Table 5. Spectral versus Spectral-Spatial Classification Results: Recall for each Tissue Type, Tumor and Healthy.
| #spectra | Spectral: LDAa | Spectral-spatial: U-Neta | |||
|---|---|---|---|---|---|
| RIGHT dataset | Tumor | 3.312 | 98.8% | 98.1% | |
| IC | 3.200 | 99.0% | 98.1% | ||
| DCIS | 112 | 94.6% | 100.0% | ||
| Healthy | 21.227 | 99.9% | 99.6% | ||
| Connective | 468 | 94.0% | 85.4% | ||
| Adipose | 20.759 | 100.0% | 99.9% | ||
| ALL dataset | Tumor | 8.883 | 83.2% | 86.3% | |
| IC | 7.400 | 83.5% | 88.3% | ||
| DCIS | 1.483 | 81.8% | 76.3% | ||
| Healthy | 75.451 | 94.2% | 94.1% | ||
| Connective | 15.731 | 77.5% | 80.0% | ||
| Adipose | 59.720 | 98.6% | 97.8% | ||
Recall = percentage of pixels that were correctly classified as either tumor or healthy tissue. Histopathologic assessment of the tissue was used as ground truth.
a
LDA and U-Net were trained on respectively the RIGHT and the ALL dataset of the training set.