<![CDATA[Skin type classification is an essential process in dermatology and skincare, aiming to categorize skin conditions such as dry, normal, and oily. However, image-based skin classification models often struggle when confronted with non-skin objects like clothing, background, or hair that are not accounted for in standard datasets. This study proposes a novel approach by integrating a nonskin class into a skin type classification model based on the EfficientNet-B0 architecture. The dataset used consists of images categorized into four classes: dry, normal, oily, and nonskin. The model was trained using transfer learning and optimized through techniques such as data augmentation, learning rate scheduling, and early stopping. The final evaluation achieved an accuracy of 91%, with the nonskin class showing perfect precision and recall. These results demonstrate that incorporating nonskin data can significantly enhance model robustness and accuracy. This research contributes a practical method for improving the reliability of skin classification systems, especially in real-world environments.]]>
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