An Empirical Study on Classification of Monkeypox Skin Lesion Detection

B. V. CHANDRAHAAS; Sachi Nandan Mohanty; Sujit Kumar Panda; Michael G.

doi:10.4108/eetpht.v8i5.3352

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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