HAI ‐Net: Skin Lesion Segmentation Using a High‐Performance Adaptive Attention and Information Interaction Network

ABSTRACT Skin lesion segmentation from dermoscopic images must be done accurately and consistently in order to diagnose diseases and arrange treatments. However, when dealing with issues like fuzzy lesion region boundaries, multiscale features, and notable variations in the lesion region's size, shape, and color, existing methods typically have high computational complexity and large parameter counts. They also frequently suffer from decreased segmentation accuracy due to inadequate capture of local features and global information. In this paper, a lightweight deep learning network based on high‐performance adaptive attention is proposed to overcome these issues. Specifically, a deep convolutional neural network is introduced to capture local information. Meanwhile, we create a high‐performance adaptive attention feature fusion module (EAAF) that uses dynamic feature selection to achieve adaptive fusion of global information with multiscale local features. Furthermore, we created a reverse dynamic feature fusion module (RDFM) at the decoding stage to efficiently fuse features at various levels while taking into account the integrity and specifics of the lesion region to increase the precision of complex lesion region segmentation. We carried out in‐depth tests on three publicly accessible datasets International Skin Imaging Collaboration (ISIC)‐2016, ISIC‐2018, and PH 2 to assess the method's efficacy and contrasted the outcomes with those of the most advanced techniques; the results confirmed that the suggested approach was superior.