Table 10 Precision, recall, and F1-scores of the best models of ten binary classifications on AZH dataset.
From: Multi-modal wound classification using wound image and location by deep neural network
Classifications | Best model(s) | Precision (%) | Recall (%) | F1-score (%) |
|---|---|---|---|---|
N–D | VGG19 + LSTM | 100 | 100 | 100 |
N–P | VGG19 + LSTM | 100 | 97.06 | 98.51 |
N–S | VGG16 + MLP | 100 | 97.62 | 98.80 |
N–V | VGG19 + LSTM | 100 | 100 | 100 |
D–P | VGG19 + LSTM | 76.19 | 94.12 | 84.21 |
D–S | VGG16 + MLP | 83.67 | 97.62 | 90.11 |
D–V | VGG16 + MLP | 92.42 | 98.39 | 95.31 |
VGG16 + LSTM | 92.42 | 98.39 | 95.31 | |
P–S | VGG16 + MLP | 86.96 | 95.24 | 90.91 |
P–V | VGG19 + MLP | 88.41 | 98.39 | 93.13 |
S–V | VGG19 + MLP | 95.38 | 100 | 97.64 |
