|
|
--- |
|
|
license: apache-2.0 |
|
|
datasets: |
|
|
- zs389/isic2016 |
|
|
- heroza/isic2017_classification |
|
|
language: |
|
|
- en |
|
|
metrics: |
|
|
- accuracy |
|
|
- precision |
|
|
- recall |
|
|
- f1 |
|
|
base_model: |
|
|
- Sadiksmart0/unet |
|
|
- glasses/densenet201 |
|
|
pipeline_tag: image-segmentation |
|
|
library: tensorflow |
|
|
model-index: |
|
|
- name: Skin-Lesion-Segmentation |
|
|
results: |
|
|
- task: |
|
|
type: image-segmentation |
|
|
dataset: |
|
|
name: isic2016 |
|
|
type: image |
|
|
metrics: |
|
|
- name: accuracy |
|
|
type: float |
|
|
value: 98.04 |
|
|
- name: precision |
|
|
type: float |
|
|
value: 97.09 |
|
|
- name: IoU (jaccard index) |
|
|
type: float |
|
|
value: 90.86 |
|
|
- name: F1-score (dice coefficient) |
|
|
type: float |
|
|
value: 94.78 |
|
|
- task: |
|
|
type: image-segmentation |
|
|
dataset: |
|
|
name: isic2017 |
|
|
type: image |
|
|
metrics: |
|
|
- name: accuracy |
|
|
type: float |
|
|
value: 93.06 |
|
|
- name: precision |
|
|
type: float |
|
|
value: 98.63 |
|
|
- name: IoU (jaccard index) |
|
|
type: float |
|
|
value: 89.97 |
|
|
- name: F1-score (dice coefficient) |
|
|
type: float |
|
|
value: 94.35 |
|
|
tags: |
|
|
- tensorflow |
|
|
- keras |
|
|
--- |
|
|
|
|
|
A precise segmentation model trained on the ISIC2016 and 2017 datasets. Throws an accuracy of 98.06% and a Jaccard Index of 90.86. Based on the U-Net architecture with a DenseNet201 backbone. |
