Melasma Segmentation using U-Net: A Precise Approach for Skin Pigment Disorder Assessment

Skin serves as a physical barrier, protecting the body from potentially hazardous exterior elements. The skin can develop several disorders, but one of the most commonly occurring and hazardous is melasma. Brown to gray-brown spots on the face, especially on the cheekbones, nasal bridge, forehead, chin, and above the top lip, are the hallmarks of the common skin disorder melasma. Early detection of melasma facilitates timely management and intervention, which can help keep the problem from getting worse. There are a variety of techniques and treatments used to prevent and treat melasma, which include both conventional and modern approaches like deep learning and machine learning. The proposed solution for melasma detection uses U-net convolutional networks. This offers an efficient and scalable method for the early detection of melasma, allowing for individualized treatment plans. The accuracy of the system is 90.97%.