SEGMENTASI LESI KULIT MONKEYPOX MENGGUNAKAN ARSITEKTUR U-NET

Monkeypox has become an issue of global concern as cases continue to rise across various countries. While most cases display mild symptoms, severe infections can lead to life-threatening complications. Early detection is therefore crucial to control the spread of monkeypox, with one approach being the segmentation of monkeypox lesions to differentiate affected areas from healthy skin and remove any interfering elements, such as hair, which can enhance model accuracy. In this consider, due to the inaccessibility of labeled masks, mask information were produced utilizing watershed segmentation with the sobel operator and otsu thresholding. The division of skin injuries was in this way carried out with a U-Net demonstrate, utilizing MobileNetV2 as the backbone and ImageNet weights for transfer learning. The U-Net model reached an accuracy of 88.07%, though some signs of overfitting were observed, likely due to low-quality label information from the watershed labeling process, which necessitates parameter tuning.