DERM12345: A Large, Multisource Dermatoscopic Skin Lesion Dataset

Dataset Description

Skin lesion datasets provide essential information for understanding various skin conditions and developing effective diagnostic tools. They aid the artificial intelligence-based early detection of skin cancer, facilitate treatment planning, and contribute to medical education and research. Published large datasets have partially coverage the subclassifications of the skin lesions. This limitation highlights the need for more expansive and varied datasets to reduce false predictions and help improve the failure analysis for skin lesions. This study presents a diverse dataset comprising 12,345 dermatoscopic images with 40 subclasses of skin lesions collected in Turkiye which comprises different skin types in the transition zone between Europe and Asia. Each subgroup contains high-resolution photos and expert annotations, providing a strong and reliable basis for future research. The detailed analysis of each subgroup provided in this study facilitates targeted research endeavors and enhances the depth of understanding regarding the skin lesions. This dataset distinguishes itself through a diverse structure with its 5 super classes, 15 main classes, 40 subclasses and 12,345 high-resolution dermatoscopic images.

• https://doi.org/10.7910/DVN/DAXZ7P

Dataset Structure

Data Splits

The dataset is divided into two splits:

• train: 9,860 images for training models.
• test: 2,485 images for evaluating model performance.

Data Fields

Each sample in the dataset contains the following fields:

• image: A PIL Image object containing the dermatoscopic image.
• image_id: A unique identifier for the image (e.g., DERM_255498).
• patient_id: A unique identifier for the patient.
• image_type: The type of image (all are dermoscopic).
• copyright-license: The license of the image (CC-BY).
• split: The split the example belongs to (train or test).
• super_class: The highest-level category of the lesion.
• malignancy: The malignancy status (benign or malignant).
• main_class_1: The primary main classification.
• main_class_2: A secondary main classification (can be null).
• sub_class: A more specific sub-classification (can be null).
• label: The most detailed subclass label, suitable for fine-grained classification tasks.

How To Use

The dataset can be easily loaded using the Hugging Face datasets library.

from datasets import load_dataset

# Load the dataset
dataset = load_dataset("DermaVLM/derm12345")
train_data = dataset['train']

# Get the first example
example = train_data[0]
image = example['image']
label = example['label']
label_name = train_data.features['label'].int2str(label)

print(f"Label: {label_name}")
image.show()

Citation

If you use this dataset in your research, please cite the original publication:

@article{yilmaz2024derm12345,
  title={Derm12345: A large, multisource dermatoscopic skin lesion dataset with 40 subclasses},
  author={Yilmaz, Abdurrahim and Yasar, Sirin Pekcan and Gencoglan, Gulsum and Temelkuran, Burak},
  journal={Scientific Data},
  volume={11},
  number={1},
  pages={1302},
  year={2024},
  publisher={Nature Publishing Group UK London}
}

@data{DVN/DAXZ7P_2024,
author = {Yilmaz, Abdurrahim and Yasar, Sirin Pekcan and Gencoglan, Gulsum and Temelkuran, Burak},
publisher = {Harvard Dataverse},
title = {{DERM12345: A Large, Multisource Dermatoscopic Skin Lesion Dataset with 40 Subclasses}},
UNF = {UNF:6:X4eUEs5UzSacwktQ3FBk+Q==},
year = {2024},
version = {V2},
doi = {10.7910/DVN/DAXZ7P},
url = {https://doi.org/10.7910/DVN/DAXZ7P}
}