Monkeypox and Measles Detection using CNN with VGG-16 Transfer Learning

M. Hafidz Ariansyah; Sri Winarno; Ramadhan Rakhmat Sani

doi:10.24191/jcrinn.v8i1.340

Monkeypox and Measles Detection using CNN with VGG-16 Transfer Learning

Authors

M. Hafidz Ariansyah Dian Nuswantoro University
Sri Winarno Dian Nuswantoro University
Ramadhan Rakhmat Sani Dian Nuswantoro University

DOI:

https://doi.org/10.24191/jcrinn.v8i1.340

Keywords:

CNN, deep learning, measles, monkeypox, vgg-16

Abstract

The Monkeypox virus causes the infectious illness monkeypox. This virus is spread by coming into touch with infected animals or humans. Monkeypox is very similar to Measles. The rubeola virus causes measles, a contagious infectious disease. The cause is what distinguishes Monkeypox from Measles sickness. Although they are both carried through the air and generate similar symptoms, Monkeypox and Measles are two separate forms of infectious diseases. Vaccination is the most effective way to prevent Measles, while for Monkeypox, no vaccine can be used to prevent infection. In differentiating Monkeypox and Measles disease, the researcher proposes an image classification to distinguish symptoms between Monkeypox and Measles. Researchers used the deep learning method of image classification with Convolutional Neural Network architecture and VGG-16 transfer learning to do the modeling. Transfer learning is a technique that allows a model which has been trained on a dataset to be used on a different dataset. It allowed the model to adapt knowledge from the original data for use in new data. Researchers propose this method because learning using deep learning is very useful for similar images so that the model can accurately predict new data. The result is that the VGG-16 model can achieve high accuracy with a value of 83.333% at epoch value = 15.

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