Classification of Monkeypox Skin Lesion using the Explainable Artificial Intelligence Assisted Convolutional Neural Networks
Abstract
The World Health Organization (WHO) has given people various protective warnings for Monkeypox. If monkeypox spreads rapidly, it becomes a serious public health problem. In this case, it creates a serious congestion in hospitals. Therefore, auxiliary systems can be needed in hospitals. In this study, explainable artificial intelligence (xAI) assisted convolutional neural networks (CNNs) based a decision support system was proposed. The data set was used for this task consists of 572 images in two classes, such as Monkeypox and Normal. 12 different CNN models were used for Monkeypox and Normal skin classification. MobileNet V2 model achieved best performance with the accuracy of 98.25%, sensitivity of 96.55%, specificity of 100.00% and F1-Score of 98.25%. This model was supported by explainable AI methods. As a result, an artificial intelligence (AI) assisted auxiliary diagnosis system has been proposed for Monkeypox skin lesion.
Thanks
This paper has been prepared by AKGUN Computer Incorporated Company. We would like to thank AKGUN Computer Inc. for providing all kinds of opportunities and funds for the execution of this project.
References
- World Health Organization. (2022). Monkeypox outbreak 2022 - Global. https://www.who.int/emergencies/situations/monkeypox-oubreak-2022
- Kumar, N., Acharya, A., Gendelman, H. E., & Byrareddy, S. N. (2022). The 2022 outbreak and the pathobiology of the monkeypox virus. Journal of Autoimmunity, 102855. https://doi.org/10.1016/j.jaut.2022.102855
- Al-Shamsi, M. (2017). Addressing the physicians’ shortage in developing countries by accelerating and reforming the medical education: Is it possible? Journal of Advances in Medical Education & Professionalism, 5(4), 210–219. /pmc/articles/PMC5611431/
- Islam, T., Hussain, M. A., Uddin, F., Chowdhury, H., & Islam, B. M. R. (2022). A Web-scraped Skin Image Database of Monkeypox, Chickenpox, Smallpox, Cowpox, and Measles. BioRxiv, 2022.08.01.502199. https://doi.org/10.1101/2022.08.01.502199
- Islam, T., Hussain, M. A., Uddin, F., Chowdhury, H., & Islam, B. M. R. (2022). Can Artificial Intelligence Detect Monkeypox from Digital Skin Images? BioRxiv, 2022.08.08.503193. https://doi.org/10.1101/2022.08.08.503193
- Ahsan, M. M., Uddin, M. R., & Luna, S. A. (2022). Monkeypox Image Data collection. https://arxiv.org/abs/2206.01774v1
- Ahsan, M. M., Uddin, M. R., Farjana, M., Sakib, A. N., Momin, K. Al, & Luna, S. A. (2022). Image Data collection and implementation of deep learning-based model in detecting Monkeypox disease using modified VGG16. https://arxiv.org/abs/2206.01862v1
- Ali, S. N., Ahmed, M. T., Paul, J., Jahan, T., Sani, S. M. S., Noor, N., & Hasan, T. (2022). Monkeypox Skin Lesion Detection Using Deep Learning Models: A Feasibility Study. https://arxiv.org/abs/2207.03342v1
- Monkeypox Skin Images Dataset (MSID) | Kaggle. (n.d.). Retrieved August 28, 2022, from https://www.kaggle.com/datasets/dipuiucse/monkeypoxskinimagedataset
Açıklanabilir Yapay Zeka Destekli Evrişimsel Sinir Ağları Kullanılarak Maymun Çiçeği Deri Lezyonunun Sınıflandırılması
Abstract
Dünya Sağlık Örgütü (DSÖ), insanlara maymun çiçeği için çeşitli koruyucu uyarılar vermiştir. Maymun çiçeği hızla yayılırsa ciddi bir halk sağlığı sorunu haline gelir. Bu durumda hastanelerde ciddi bir yoğunluk oluşturur. Bu nedenle, hastanelerde yardımcı sistemlere ihtiyaç duyulabilir. Bu çalışmada, açıklanabilir yapay zeka (AYZ) destekli evrişimli sinir ağları (ESA) tabanlı bir karar destek sistemi önerilmiştir. Bunun için kullanılan veri seti Monkeypox ve Normal olmak üzere iki sınıfta 572 görüntüden oluşmaktadır. Monkeypox ve Normal ciltlerin sınıflandırılması için 12 farklı ESA modeli kullanılmıştır. MobileNet V2 modeli, %98,25 doğruluk, %96,55 duyarlılık, %100,00 özgüllük ve %98,25 F1-Skoru ile en iyi performansı elde etmiştir. Bu model, AYZ yöntemleriyle desteklenmiştir. Sonuç olarak, maymun çiçeği cilt lezyonu için yapay zeka (YZ) destekli bir yardımcı teşhis sistemi önerilmiştir.
References
- World Health Organization. (2022). Monkeypox outbreak 2022 - Global. https://www.who.int/emergencies/situations/monkeypox-oubreak-2022
- Kumar, N., Acharya, A., Gendelman, H. E., & Byrareddy, S. N. (2022). The 2022 outbreak and the pathobiology of the monkeypox virus. Journal of Autoimmunity, 102855. https://doi.org/10.1016/j.jaut.2022.102855
- Al-Shamsi, M. (2017). Addressing the physicians’ shortage in developing countries by accelerating and reforming the medical education: Is it possible? Journal of Advances in Medical Education & Professionalism, 5(4), 210–219. /pmc/articles/PMC5611431/
- Islam, T., Hussain, M. A., Uddin, F., Chowdhury, H., & Islam, B. M. R. (2022). A Web-scraped Skin Image Database of Monkeypox, Chickenpox, Smallpox, Cowpox, and Measles. BioRxiv, 2022.08.01.502199. https://doi.org/10.1101/2022.08.01.502199
- Islam, T., Hussain, M. A., Uddin, F., Chowdhury, H., & Islam, B. M. R. (2022). Can Artificial Intelligence Detect Monkeypox from Digital Skin Images? BioRxiv, 2022.08.08.503193. https://doi.org/10.1101/2022.08.08.503193
- Ahsan, M. M., Uddin, M. R., & Luna, S. A. (2022). Monkeypox Image Data collection. https://arxiv.org/abs/2206.01774v1
- Ahsan, M. M., Uddin, M. R., Farjana, M., Sakib, A. N., Momin, K. Al, & Luna, S. A. (2022). Image Data collection and implementation of deep learning-based model in detecting Monkeypox disease using modified VGG16. https://arxiv.org/abs/2206.01862v1
- Ali, S. N., Ahmed, M. T., Paul, J., Jahan, T., Sani, S. M. S., Noor, N., & Hasan, T. (2022). Monkeypox Skin Lesion Detection Using Deep Learning Models: A Feasibility Study. https://arxiv.org/abs/2207.03342v1
- Monkeypox Skin Images Dataset (MSID) | Kaggle. (n.d.). Retrieved August 28, 2022, from https://www.kaggle.com/datasets/dipuiucse/monkeypoxskinimagedataset
Details
| Primary Language | English |
|---|---|
| Subjects | Engineering |
| Journal Section | Articles |
| Authors | |
| Early Pub Date | September 26, 2022 |
| Publication Date | September 30, 2022 |
| Published in Issue | Year 2022 Issue: 40 |
