Abstract

Melanoma, a highly aggressive form of skin cancer, necessitates early and accurate diagnosis for effective treatment. This paper presents an innovative approach to melanoma diagnosis through skin lesion segmentation. Leveraging the synergistic potential of perceptual color difference saliency and morphological analysis, our proposed method aims to enhance the precision of melanoma lesion identification. By harnessing advanced artificial intelligence algorithms, we demonstrate the capability of automated lesion segmentation, enabling clinicians to discern malignancies from healthy tissue with heightened accuracy. This research contributes to the growing field of medical image analysis, providing a robust framework for improving melanoma diagnosis and patient outcomes.