An Empirical Study on Classification of Monkeypox Skin Lesion Detection

Authors

  • B. V. CHANDRAHAAS School of Computer Science & Engineering (SCOPE), VIT-AP University, Amaravati, Andhra Pradesh, India
  • Sachi Nandan Mohanty School of Computer Science & Engineering (SCOPE), VIT-AP University, Amaravati, Andhra Pradesh, India
  • Sujit Kumar Panda GIFT Autonomous College, Bhubaneswar, Odisha, India
  • Michael G. Department of Computational Intelligence, Saveetha School of Engineering, SIMATS, Chennai, India

DOI:

https://doi.org/10.4108/eetpht.v8i5.3352

Keywords:

Deep learning, Disease diagnosis, Image processing, Monkeypox virus, Machine learning, Transfer learning, CNN models, Computer vision architectures

Abstract

INTRODUCTION: After the covid-19 outbreak, Monkeypox has become a global pandemic putting people’s lives in jeopardy. Monkeypox has become a major concern in 40+ countries apart from Africa as scientists are struggling to clinically diagnose the virus as it looks similar with chickenpox and measles. As a part of our research, we found that to get the clinically tested result of monkey pox through polymerase chain reaction (PCR) test would take 3-4 days which is a lengthy process.

OBJECTIVES: The objective of this paper is to provide a rapid identification solution which can instantly detect monkeypox virus with the help of computer vision architectures. This can be considered for preliminary examination of skin lesions and help the victim isolate themselves so that they would be cautious and can stop the spreading of virus.

METHODS: Many studies have been conducted to identify the monkeypox with the help of Deep Learning models but in this study, we compare the test results obtained by deep learning CNN models AlexNet, GoogLeNet using transfer learning approach and determine the efficient model[2].

RESULTS: Testing the algorithms by changing the batch sizes and number of epochs we have obtained a highest accuracy of 83.61% for AlexNet and 82.64% for GoogLeNet.

CONCLUSION: AlexNet was outperforming GoogLeNet architecture in terms of validation accuracy thus providing better results.

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References

Ahsan, M. M., Uddin, M. R., Farjana, M., Sakib, A. N., Momin, K. A., & Luna, S. A. (2022). Image Data collection and implementation of deep learning-based model in detecting Monkeypox disease using modified VGG16. arXiv preprint arXiv:2206.01862.

Ali, S. N., Ahmed, M., Paul, J., Jahan, T., Sani, S. M., Noor, N., & Hasan, T. (2022). Monkeypox skin lesion detection using deep learning models: A feasibility study. arXiv preprint arXiv:2207.03342.

McCollum, A. M., & Damon, I. K. (2014). Human monkeypox. Clinical infectious dieases, 58(2), 260-267.4. DOI: https://doi.org/10.1093/cid/cit703

Alakunle, E., Moens, U., Nchinda, G., & Okeke, M. I. (2020). Monkeypox virus in Nigeria: infection biology, epidemiology, and evolution. Viruses, 12(11), 1257. DOI: https://doi.org/10.3390/v12111257

Nolen, L. D., Osadebe, L., Katomba, J., Likofata, J., Mukadi, D., Monroe, B., ... & Reynolds, M. G. (2016). Extended human-to-human transmission during a monkeypox outbreak in the Democratic Republic of the Congo. Emerging infectious diseases, 22(6), 1014.7. Monkeypox signs and symptoms. (accessed on may 30, 2022). https://www.cdc.gov/poxvirus/ monkeypox/symptoms.html, 2022. DOI: https://doi.org/10.3201/eid2206.150579

Ahsan, M. M., E. Alam, T., Trafalis, T., & Huebner, P. (2020). Deep MLP-CNN model using mixed-data to distinguish between COVID-19 and Non-COVID-19 patients. Symmetry, 12(9), 1526. DOI: https://doi.org/10.3390/sym12091526

Ahsan, M. M., & Siddique, Z. (2022). Machine learning-based heart disease diagnosis: A systematic literature review. Artificial Intelligence in Medicine, 102289.

Gisele Helena Barboni Miranda and Joaquim Cezar Felipe. Computer-aided diagnosis system based on fuzzy logic for breast cancer categorization. Computers in biology and medicine, 64:334–346, 2015. DOI: https://doi.org/10.1016/j.compbiomed.2014.10.006

Ardakani, A. A., Kanafi, A. R., Acharya, U. R., Khadem, N., & Mohammadi, A. (2020). Application of deep learning technique to manage COVID-19 in routine clinical practice using CT images: Results of 10 convolutional neural networks. Computers in biology and medicine, 121, 103795. DOI: https://doi.org/10.1016/j.compbiomed.2020.103795

Wang, L., Lin, Z. Q., & Wong, A. (2020). Covid-net: A tailored deep convolutional neural network design for detection of covid-19 cases from chest x-ray images. Scientific Reports, 10(1), 1-12. DOI: https://doi.org/10.1038/s41598-020-76550-z

Multi-country monkeypox outbreak in non-endemic countries.(accessed on may 29, 2022). https://www.who. int/emergencies/disease-outbreak-news/item/2022-DON385, 2022.

Ahsan, M. M., & Siddique, Z. (2022). Machine learning-based heart disease diagnosis: A systematic literature review. Artificial Intelligence in Medicine, 102289. DOI: https://doi.org/10.1016/j.artmed.2022.102289

Perez, L., & Wang, J. (2017). The effectiveness of data augmentation in image classification using deep learning. arXiv preprint arXiv:1712.04621.

Adler, H., Gould, S., Hine, P., Snell, L. B., Wong, W., Houlihan, C. F., ... & Hruby, D. E. (2022). Clinical features and management of human monkeypox: a retrospective observational study in the UK. The Lancet Infectious Diseases.. DOI: https://doi.org/10.1016/S1473-3099(22)00228-6

“The World Health Network Declares Monkeypox A Pandemic - Press

Release— June 22, 2022,” 2022, [Online]. Available: https://www.

worldhealthnetwork.global/monkeypoxpressrelease.

Reed, K. D., Melski, J. W., Graham, M. B., Regnery, R. L., Sotir, M. J., Wegner, M. V., ... & Damon, I. K. (2004). The detection of monkeypox in humans in the Western Hemisphere. New England Journal of Medicine, 350(4), 342-350. DOI: https://doi.org/10.1056/NEJMoa032299

Altindis, M., Puca, E., & Shapo, L. (2022). Diagnosis of monkeypox virus–An overview. Travel medicine and infectious disease, 102459. DOI: https://doi.org/10.1016/j.tmaid.2022.102459

Peiró-Mestres, A., Fuertes, I., Camprubí-Ferrer, D., Marcos, M. Á., Vilella, A., Navarro, M., ... & Hospital Clinic de Barcelona Monkeypox Study Group. (2022). Frequent detection of monkeypox virus DNA in saliva, semen, and other clinical samples from 12 patients, Barcelona, Spain, May to June 2022. Eurosurveillance, 27(28), 2200503. DOI: https://doi.org/10.2807/1560-7917.ES.2022.27.28.2200503

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Published

25-05-2023

How to Cite

1.
CHANDRAHAAS BV, Mohanty SN, Panda SK, G. M. An Empirical Study on Classification of Monkeypox Skin Lesion Detection. EAI Endorsed Trans Perv Health Tech [Internet]. 2023 May 25 [cited 2024 Apr. 25];9:e4. Available from: https://publications.eai.eu/index.php/phat/article/view/3352