Efficient and real-time skin lesion image segmentation using spatial-frequency information and channel convolutional networks

Accurate segmentation of skin lesions is essential for physicians to screen in dermoscopy images. However, they commonly face three main limitations: difficulty in accurately processing targets with coarse edges; frequent challenges in recovering detailed feature data; and a lack of adequate capability for the effective amalgamation of multi-scale features. To overcome these problems, we propose a skin lesion segmentation network (SFCC Net) that combines an attention mechanism and a redundancy reduction strategy. The initial step involved the design of a downsampling encoder and an encoder composed of Receptive Field (REFC) Blocks, aimed at supplementing lost details and extracting latent features. Subsequently, the Spatial-Frequency-Channel (SF) Block was employed to minimize feature redundancy and restore fine-grained information. To fully leverage previously learned features, an Up-sampling Convolution (UpC) Block was designed for information integration. The network’s performance was compared with state-of-the-art models on four public datasets. Experimental results demonstrate significant improvements in the network’s performance. On the ISIC datasets, the proposed network outperformed D-LKA Net by 4.19%, 0.19%, and 7.75% in F1, and by 2.14%, 0.51%, and 12.20% in IoU. The frame rate (FPS) of the proposed network when processing skin lesion images underscores its suitability for real-time image analysis. Additionally, the network’s generalization capability was validated on a lung dataset.