Prediction of Monkeypox on the Skin Lesion with the Siamese Deep Learning Model

Yıl 2023, , 225 - 231, 21.08.2023

Talha Burak Alakuş

https://doi.org/10.17694/bajece.1255798

Cited By: 1

Öz

One of the viral diseases that started to cause concern in various parts of the world after the COVID-19 pandemic is the monkeypox virus, which has recently emerged. The virus, which was known in previous years and mostly seen in the Western and Central parts of the African continent, has recently begun to affect different human populations in different ways. Monkeypox is transmitted to humans from an animal infected with the virus or from another human being infected with monkeypox. Among the most basic symptoms are high fever, back and muscle aches, chills, and blisters on the skin. These blisters seen on the skin are sometimes confused with chickenpox and measles, and this causes the diagnosis and, accordingly, the treatment process to be wrong. Therefore, the need for computer-aided systems has increased and the need for more robust and reliable approaches has arisen. In this study, using the deep learning model, the distinction of the blisters seen in the body was made and it was decided whether the disease was monkeypox or another disease (chickenpox and measles). The study consisted of three stages. In the first stage, data were obtained and images of both chickenpox and other diseases were used. In the second stage, the Siamese deep learning model was used, and data were classified. In the last stage, the performance of the classifier was evaluated and accordingly accuracy, precision, recall, F1-score, and confusion matrix were used. At the end of the study, an accuracy score of 91.09% was obtained. This result showed that the developed deep learning-based model can be used in this field.

Anahtar Kelimeler

Artificial intelligence, Monkeypox, Siamese deep learning model

Kaynakça

• REFERENCES [1] J.G. Breman, R. Kalisa, M.V. Steniowski, E. Zanotto, A.I. Gromyko, et al. “Human monkeypox, 1970-79.” Bulletin of the World Health Organization, vol. 58. 2, 1980, pp. 165-182.
• [2] D. Mileto, A. Riva, M. Cutrera, D. Moschese, D. Mancon, et al. “New challenges in human monkeypox outside Africa: A review and case report from Italy.” Travel Medicine and Infectious Disease, vol. 49, 2022, pp. 1-10.
• [3] J.P. Thornhill, S. Barkati, S. Walmsley, J. Rockstroh, A. Antinori, et al. “Monkeypox virus infection in human across 16 countries-April-June-2022.” The New England Journal of Medicine, vol. 387, 2022, pp. 687-691.
• [4] J.A. Cann, P.B. Jahrling, L.E. Hensley, V. Wahl-Jensen. “Comparative pathology of smallpox and monkeypox in man and macaques.” Journal of Comparative Pathology, vol. 148. 1, 2013, pp. 6-21.
• [5] J.G. Rizk, G. Lippi, B.M. Henry, D.N. Forthal, Y. Rizk. “Prevention and treatment of monkeypox.” Drugs, vol. 82, 2022, pp. 957-963.
• [6] A. Zumla, S.R. Valdoleiros, N. Haider, D. Asogun, F. Ntoumi, et al. “Monkeypox outbreaks outside endemic regions: Scientific and social priorities.” The Lancet: Infectious Diseases, vol. 22. 7, 2022, pp. 929-931.
• [7] K.C. Zachary, E.S. Shenoy. “Monkeypox transmission following exposure in healthcare facilities in nonendemic settings: Low risk but limited literature.” Infection Control and Hospital Epidemiology, vol. 43. 7, 2022, pp. 920-924.
• [8] D. Daskalakis, R.P. McClung, L. Mena, J. Mermin. “Monkeypox: Avoiding the mistakes of past infectious disease epidemics.” Annals of Internal Medicine, vol. 175. 8, 2022, pp. 1177-1178.
• [9] Z. Jezek, M. Szczeniowski, K.M. Palukulu, M. Mutombo, B. Grab. “Human monkeypox: Confusion with chickenpox.” Acta Tropica, vol. 45. 4, 1988, pp. 297-307.
• [10] S.N. Shchelkunov, A.V. Totmenin, I.V. Babkin, P.F. Safronov, O.I. Ryazankina, et al. “Human monkeypox and smallpox viruses: Genomic comparison.” FEBS Letters, vol. 509. 1, 2011, pp. 66-70.
• [11] S.N. Ali, T. Ahmed, J. Paul, T. Jahan, M.S. Sani, et al. “Monkeypox skin lesion detection using deep learning models: A feasibility study.” arXiv, 2022.
• [12] C. Sitaula, T.B. Shani. “Monkeypox virus detection using pre-trained deep learning-based approaches.” arXiv, 2022.
• [13] M. Ahsan, M. Farjana, K.A. Momin, M.R. Uddin, A.N. Sakib, et al. “Image data collection and implementation of deep learning-based model in detecting monkeypox disease using modified VGG16.” arXiv, 2022.
• [14] V. Kumar. “Analysis of CNN features with multiple machine learning classifiers in diagnosis of monkeypox from digital skin images”. medRxiv, 2022.
• [15] S.L. Munoz, L.E. Escobar, M.J. Civit, P.F. Luna, A. Civit, et al. “Monkeypox diagnostic-aid system with skin images using convolutional neural networks.” SSRN, 2022.
• [16] R. Vinayakumar, K.P. Soman. “Siamese neural networks architecture for homoglyph attacks detection.” ICT Express, vol. 6. 1, 2020, pp. 16-19.
• [17] J. Bromley, J.W. Bentz, L. Bottou, I. Guyon, Y. Lecun, et al. “Signature verification using a “siamese” time delay neural network.” International Journal of Pattern Recognition and Artificial Intelligence, vol. 7. 4, 1993, pp. 669-686.
• [18] M. Toğaçar, Z. Cömert, B. Ergen. “Recognition of the digits in Turkish sign language using siamese neural networks.” Dokuz Eylül University Faculty of Engineering Journal of Science and Engineering, vol. 23. 68, 2021, pp. 349-356.
• [19] I. Melekhov, J. Kannala, E. Rahtu. “Siamese network features for image matching.” International Conference on Pattern Recognition. Cancun, Mexico, 2016.
• [20] B. Wang, D. Wang. “Plant leaves classification: A few-shot learning method based on siamese network.” IEEE Access, vol. 7, 2019, pp. 151754-151763.
• [21] X. Dong, J. Shen. “Triplet loss in siamese network for object tracking.” European Conference on Computer Vision. Munich, Germany, 2018.
• [22] Apple Unleashes M1, https://www.apple.com/newsroom/2020/11/apple-unleashes-m1/, Erişim tarihi (29 Mayıs 2023)
• [23] D. Kasperek, M. Podpora, A. Kawala-Sterniuk. “Comparison of the usability of apple M1 processors for various machine learning tasks.” Sensors, vol. 22, 2022.
• [24] L.A. Jeni, J.F. Cohn, F. De La Torre. “Facing imbalanced data--recommendations for the use of performance metrics.” International Conference on Affective Computing and Intelligent Interaction and Workshops. Geneva, Switzerland, 2013.