--- 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 ClĂ­nic 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 ```