<![CDATA[Skin diseases on the face are common health problems that can affect appearance and self-confidence, and early detection is important to prevent more severe conditions. This study proposes a facial skin disease early detection application based on Python using Deep Learning YOLOv8 and Convolutional Neural Network (CNN). YOLOv8 is utilized to detect facial areas and visualize detection results using bounding boxes, while CNN is employed to classify facial skin conditions based on extracted visual features. The system allows users to upload facial images through a graphical user interface and provides detection results in the form of disease labels, confidence scores, and descriptive information. Experimental results show that YOLOv8 is capable of detecting facial objects in real time, while the CNN classification achieves an average accuracy of approximately 88%, as evaluated using a confusion matrix and performance metrics such as precision, recall, and F1-score. The combination of YOLOv8 and CNN enhances the effectiveness of facial skin disease detection by providing both localization and accurate classification. This system can serve as a supportive tool for early identification of facial skin diseases prior to professional medical diagnosis.]]>
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