Table 1.
Melanoma detection performance using the ISIC 2017 dataset. Performance is shown for six classifiers and two feature types (conventional features (Conv) [36], and the combination of conventional and unweighted graph features (Graph+Conv)). Performance metrics are the accuracy (AC), sensitivity (SN) and specificity (SP)
| SVM | SVM | SVM | KNN | MLP | RF | ||
|---|---|---|---|---|---|---|---|
| (RBF) | (Sigmoid) | (Poly) | |||||
| AC | 54.53 | 61.40 | 52.51 | 52.32 | 56.76 | 47.30 | |
| Conv | AUC | 56.26 | 57.01 | 57.00 | 46.53 | 52.09 | 49.68 |
| SN | 40.15 | 64.48 | 23.94 | 39.77 | 38.22 | 1.93 | |
| SP | 72.20 | 60.62 | 83.40 | 66.80 | 77.99 | 94.59 | |
| AC | 50.58 | 61.97 | 52.51 | 52.53 | 51.54 | 48.07 | |
| Graph | AUC | 53.52 | 50.00 | 50.03 | 49.59 | 53.55 | 53.84 |
| +Conv | SN | 34.36 | 67.18 | 13.13 | 35.52 | 25.87 | 1.02 |
| SP | 72.54 | 59.46 | 93.82 | 71.81 | 80.69 | 96.14 |