README.md

---
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.