Application of AI Using UNet in Skin Lesion Segmentation

Abstract

Skin lesion segmentation is a critical task in dermatology, essential for the accurate diagnosis and treatment of various skin conditions, including skin cancer. The precise identification of lesion boundaries in medical images significantly helps in early detection and effective management of these conditions. In this study, a U-Net model was employed to perform segmentation of skin lesions, using its advanced encoder-decoder architecture and skip connections to capture fine details and spatial hierarchies within the images. The model yielded an overall accuracy of 95.47%, a precision of 97.21%, a recall of 84.04%, and an F1 score of 90.15%. These results affirm the U-Net model's proficiency in accurately segmenting skin lesions. The precise boundary delineation provided by the model can help healthcare professionals detect malignant lesions, which often exhibit irregular boundaries, thereby improving diagnostic accuracy. The integration of this model into clinical practice can enhance diagnostic accuracy and efficiency, reduce the workload on healthcare professionals, and improve patient outcomes. The promising performance of the U-Net model emphasizes its potential to revolutionize dermatological diagnostics and support healthcare professionals in delivering timely and precise patient care.