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---
license: mit
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': Actinic keratoses
'1': Basal cell carcinoma
'2': Benign keratosis-like lesions
'3': Chickenpox
'4': Cowpox
'5': Dermatofibroma
'6': HFMD
'7': Healthy
'8': Measles
'9': Melanocytic nevi
'10': Melanoma
'11': Monkeypox
'12': Squamous cell carcinoma
'13': Vascular lesions
splits:
- name: train
num_bytes: 11781822388.236
num_examples: 29322
- name: validation
num_bytes: 1129580056.38
num_examples: 3660
- name: test
num_bytes: 1166877801.52
num_examples: 3674
download_size: 9960809758
dataset_size: 14078280246.136002
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
---
# Skin Lesions Dataset
A dataset for 14 types of skin lesions classification consisted of merging [HAM10000(2019)](https://www.kaggle.com/datasets/andrewmvd/isic-2019) and [MSLDv2.0](https://www.kaggle.com/datasets/joydippaul/mpox-skin-lesion-dataset-version-20-msld-v20)
The dataset consisted of 14 categories:
- Actinic keratoses
- Basal cell carcinoma
- Benign keratosis-like-lesions
- Chickenpox
- Cowpox
- Dermatofibroma
- Healthy
- HFMD
- Measles
- Melanocytic nevi
- Melanoma
- Monkeypox
- Squamous cell carcinoma
- Vascular lesions
## Load the dataset
```python
from datasets import load_dataset
dataset = load_dataset("ahmed-ai/skin-lesions-dataset")
```
Citation for the original datasets
### MSLDv2.0
```
@article{Nafisa2023,
title={A Web-based Mpox Skin Lesion Detection System Using State-of-the-art Deep Learning Models Considering Racial Diversity},
author={Ali, Shams Nafisa and Ahmed, Md. Tazuddin and Jahan, Tasnim and Paul, Joydip and Sani, S. M. Sakeef and Noor, Nawshaba and Asma, Anzirun Nahar and Hasan, Taufiq},
journal={arXiv preprint arXiv:2306.14169},
year={2023}
}
```
### HAM10000 (2019)
```
BCN_20000 Dataset: (c) Department of Dermatology, Hospital Clínic de Barcelona
HAM10000 Dataset: (c) by ViDIR Group, Department of Dermatology, Medical University of Vienna; https://doi.org/10.1038/sdata.2018.161
MSK Dataset: (c) Anonymous; https://arxiv.org/abs/1710.05006; https://arxiv.org/abs/1902.03368
```
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