An updated version of the MSLD dataset, MSLD v2.0 has been released after being verified by an expert dermatologist!

For details, check our GitHub repo!

Context

The recent monkeypox outbreak has become a global healthcare concern owing to its rapid spread in more than 65 countries around the globe. To obstruct its expeditious pace, early diagnosis is a must. But the confirmatory Polymerase Chain Reaction (PCR) tests and other biochemical assays are not readily available in sufficient quantities. In this scenario, computer-aided monkeypox identification from skin lesion images can be a beneficial measure. Nevertheless, so far, such datasets are not available. Hence, the "Monkeypox Skin Lesion Dataset (MSLD)" is created by collecting and processing images from different means of web-scrapping i.e., from news portals, websites and publicly accessible case reports.

The creation of "Monkeypox Image Lesion Dataset" is primarily focused on distinguishing the monkeypox cases from the similar non-monkeypox cases. Therefore, along with the 'Monkeypox' class, we included skin lesion images of 'Chickenpox' and 'Measles' because of their resemblance to the monkeypox rash and pustules in initial state in another class named 'Others' to perform binary classification.

Content

There are 3 folders in the dataset.

1) Original Images: It contains a total number of 228 images, among which 102 belongs to the 'Monkeypox' class and the remaining 126 represents the 'Others' class i.e., non-monkeypox (chickenpox and measles) cases.

2) Augmented Images: To aid the classification task, several data augmentation methods such as rotation, translation, reflection, shear, hue, saturation, contrast and brightness jitter, noise, scaling etc. have been applied using MATLAB R2020a. Although this can be readily done using ImageGenerator/other image augmentors, to ensure reproducibility of the results, the augmented images are provided in this folder. Post-augmentation, the number of images increased by approximately 14-folds. The classes 'Monkeypox' and 'Others' have 1428 and 1764 images, respectively.

3) Fold1: One of the three-fold cross validation datasets. To avoid any sort of bias in training, three-fold cross validation was performed. The original images were split into training, validation and test set(s) with the approximate proportion of 70 : 10 : 20 while maintaining patient independence. According to the commonly perceived data preparation practice, only the training and validation images were augmented while the test set contained only the original images. Users have the option of using the folds directly or using the original data and employing other algorithms to augment it.

Additionally, a CSV file is provided that has 228 rows and two columns. The table contains the list of all the ImageID(s) with their corresponding label.

Web Application

Since monkeypox is demonstrating a very rapid community transmission pattern, a consumer-level software is truly necessary to increase awareness and encourage people to take rapid action. We have developed an easy-to-use web application named Monkey Pox Detector using the open-source python streamlit framework that uses our trained model to address this issue. It makes predictions on whether or not to see a specialist along with the prediction accuracy. Future updates will benefit from the user data we continue to collect and use to improve our model. The web app has a flask core, so that it can be deployed cross-platform in the future.

Learn more at our GitHub repo!

Citation

If this dataset helped your research, please cite the following articles:

Ali, S. N., Ahmed, M. T., Paul, J., Jahan, T., Sani, S. M. Sakeef, Noor, N., & Hasan, T. (2022). Monkeypox Skin Lesion Detection Using Deep Learning Models: A Preliminary Feasibility Study. arXiv preprint arXiv:2207.03342.

@article{Nafisa2022,
title={Monkeypox Skin Lesion Detection Using Deep Learning Models: A Preliminary Feasibility Study},
author={Ali, Shams Nafisa and Ahmed, Md. Tazuddin and Paul, Joydip and Jahan, Tasnim and Sani, S. M. Sakeef and Noor, Nawshaba and Hasan, Taufiq},
journal={arXiv preprint arXiv:2207.03342},
year={2022}
}

Ali, S. N., Ahmed, M. T., Jahan, T., Paul, J., Sani, S. M. Sakeef, Noor, N., Asma, A. N., & Hasan, T. (2023). A Web-based Mpox Skin Lesion Detection System Using State-of-the-art Deep Learning Models Considering Racial Diversity. arXiv preprint arXiv:2306.14169.

@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}
}