Implementation of a Vision Transformer for Web-Based Acne Type Classification Using Facial Images
DOI:
https://doi.org/10.37859/coscitech.v7i2.11982
Abstract
Acne Vulgaris is a widespread skin condition that can affect not only physical skin health but also a person's self-confidence. Because manually distinguishing acne types still demands specialized expertise, an artificial-intelligence-based approach is needed to support this classification task. This research applies a Vision Transformer (ViT-B/16) architecture to categorize acne lesions from facial images into four groups: normal, papule, pustule, and nodule. A total of 4,000 images were used as the dataset and processed through a transfer-learning strategy initialized with pre-trained ImageNet weights. The model was trained across 20 epochs with the Adam optimizer and a learning rate of 0.001. Testing showed that the model reached an accuracy of 86%. The resulting model was then embedded into a web-based application to streamline the acne identification workflow. These outcomes confirm that Vision Transformer can classify acne types reliably and holds promise as an automated early-screening tool for facial skin conditions.
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References
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