Enhancing Monkeypox Skin Lesion Classification With Resnet50v2: The Impact Of Pre-Trained Models From Medical And General Domains

Saifulloh  Azhar; Abdul Syukur; M. Arief Soeleman; Affandy Affandy; Aris Marjuni

doi:10.52436/1.jutif.2025.6.3.4486

Authors

Saifulloh Azhar Faculty of Computer Science, Universitas Dian Nuswantoro, Semarang, Indonesia
Abdul Syukur Faculty of Computer Science, Universitas Dian Nuswantoro, Semarang, Indonesia
M. Arief Soeleman Faculty of Computer Science, Universitas Dian Nuswantoro, Semarang, Indonesia
Affandy Faculty of Computer Science, Universitas Dian Nuswantoro, Semarang, Indonesia
Aris Marjuni Faculty of Computer Science, Universitas Dian Nuswantoro, Semarang, Indonesia

DOI:

https://doi.org/10.52436/1.jutif.2025.6.3.4486

Keywords:

Dermatology, HAM10000, ImageNet, Monkeypox, ResNet50v2

Abstract

The monkeypox outbreak has emerged as a pressing global health concern, as evidenced by the rising number of cases reported in various countries. This rare zoonotic disease, caused by the Monkeypox virus (MPXV) of the Poxviridae family, is commonly found in Africa. However, since 2022, cases have also spread to various countries, including Indonesia. The dermatological symptoms exhibited by affected individuals vary, with the potential for further transmission through contamination. Early and accurate detection of monkey pox disease is therefore essential for effective treatment. The present study aims to improve the classification of Monkey Pox using the modified Resnet50V2 model, trained using pre-training datasets namely ImageNet and HAM10000, where batch size and learning rate parameters were adjusted. The study achieved high accuracy in distinguishing monkeypox cases, with 98.43% accuracy for Resnet50V2 with pretrained ImageNet and 70.57% accuracy for Resnet50V2 with pretrained HAM10000. Future research will focus on refining these models, exploring hybrid approaches incorporating convolutional neural networks, this advancement contributes to the development of automated early diagnosis tools for monkeypox skin conditions, especially in resource-limited clinical settings where access to dermatology experts is limited.

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