Research Article
Trichoscopy of Alopecia Areata: Hair Loss Feature Extraction and Computation Using Grid Line Selection and Eigenvalue
Table 1
Evaluation of Input data in Figure
8. Bold data are assumed as bad experiment results and italic data as good experiment results.
| | Input (a) | Input (b) | Input (c) | Input (d) | Input (e) | Input (f) | Dataset |
| Hair truth | 18 | 20 | 23 | 23 | 17 | 25 | 19.59 (avg) | Hair prediction | 15.42 | 22.49 | 22.73 | 12.97 | 15.21 | 23.12 | 19.04 (avg) | Hair difference | 2.59 | 2.49 | 0.27 | 10.03 | 1.79 | 1.88 | 3.13 | Follicle truth | 9 | 8 | 7 | 7 | 8 | 7 | 8.15 (avg) | Follicle prediction | 9 | 12 | 8 | 7 | 10 | 6 | 8.49 (avg) | Follicle difference | 0 | 4 | 1 | 0 | 2 | 1 | 1.44 | Thickness truth | 53.8168 | 67.0311 | 72.8527 | 76.7309 | 66.6854 | 60.6011 | ā | Thickness prediction | 61.0024 | 65.3314 | 67.3158 | 87.0331 | 58.4213 | 60.4601 | 61.98 | Thickness difference | 7.1856 | 1.6997 | 5.5369 | 10.3022 | 8.2641 | 0.141 | ā | Total accuracy | 90.75 | 78.34 | 92.31 | 80.99 | 84.03 | 92.65 | 96.51 (avg) | | | | | | | | 83.20 (diff) |
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