Abstract
In human communication, the resource of primary information can be read from a human’s face. Health problems occur in line with age, and one way to detect health issues is through changes in facial skin. People typically pay less attention to initial facial skin changes, even though the changes might be linked to a particular disease, such as Lupus. Treatment for Lupus takes time and is costly. Cutting-edge technology and Artificial Intelligence (AI) bring a new horizon to the medical field where a disease can be detected or predicted early. This paper presents the classification of training images for the early detection of middle-aged unhealthy facial skin. The Multi-Task Cascaded Convolutional Neural Networks (MTCNN) technique provides the face boundary box and the performance of face detection uses VGG19 architecture. The images dataset was divided into data training and data testing with a ratio of 80%: 20%, respectively, and the healthy or unhealthy face image was determined with a machine learning approach. The experimental results showed that the accuracy of the proposed Support Vector Machine classifier model was 92.2%.
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Wiryasaputra, R., Huang, CY., Williyanto, R., Yang, CT. (2022). Prediction of Middle-Aged Unhealthy Facial Skin Using VGG19 and Support Vector Machine Models. In: Hsieh, SY., Hung, LJ., Klasing, R., Lee, CW., Peng, SL. (eds) New Trends in Computer Technologies and Applications. ICS 2022. Communications in Computer and Information Science, vol 1723. Springer, Singapore. https://doi.org/10.1007/978-981-19-9582-8_25
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DOI: https://doi.org/10.1007/978-981-19-9582-8_25
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