Seg-SkiNet: adaptive deformable fusion convolutional network for skin lesion segmentation.

ABSTRACT:

Background

Skin lesion segmentation plays a significant role in skin cancer diagnosis. However, due to the complex shapes, varying sizes, and different color depths, precise segmentation of skin lesions is a challenging task. Therefore, the aim of this study was to design a customized deep learning (DL) model for the precise segmentation of skin lesions, particularly for complex shapes and small target lesions.

Methods

In this study, an adaptive deformable fusion convolutional network (Seg-SkiNet) was proposed. Seg-SkiNet integrated dual-channel convolution encoder (Dual-Conv encoder), Multi-Scale-Multi-Receptive Field Extraction and Refinement (Multi²ER) module, and local-global information interaction fusion decoder (LGI-FSN decoder). In the Dual-Conv encoder, a

SUBMITTER: Nan H 

PROVIDER: S-EPMC11744141 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature