Context-Aware Skin Lesion Segmentation with U2Net and Image Standardization

Skin cancer is a major global cause of mortality. This paper introduces a new deep learning (DL) framework of using
$U^{2}$
-Net with image standardization, for early detection of malignant skin lesions to reduce mortality rates. The proposed approach employs a two-level nested U -structure, which differs from the conventional U-Net and transfer learning architectures. By integrating receptive fields of various sizes within Residual U-blocks,
$U^{2}$
Net model captures contextual information from multiple scales. Additionally, while incorporated with image standardization, the model thus more effectively generalize global features. Early experimental results demonstrate the potential of the proposed framework for highly accurate automatic skin lesion segmentation on the H AM 1 0000 dataset with mean scores of 97.50% for accuracy, 94.62% for dice score, 89.63% for IOU, and 95.70% for recall. The proposed
$U^{2}$
Net framework advances the field of skin cancer detection, with the promise of reducing its mortality rate.