SEHC-Net: Network for Image Segmentation Based on Information Compensation and Perceptual Enhancement

Using U-Net as the backbone, a novel medical image segmentation network called SEHC-Net is proposed for medical image segmentation of melanoma. A new structure named Sense and Edge Boost Module (SEBM) is designed specifically to address the challenges in segmenting melanoma images having irregular shapes, diverse sizes, and blurry boundaries. SEBM can expand the receptive fields of features, which enhances the model's ability to extract the target edge information and further capture the connections between pixels. Additionally, a hierarchical compensation module is proposed to solve the problem of information redundancy caused by long connections during information concatenation. This can compensate for the defect that mainstream segmentation networks cannot fully balance spatial contextual information and high-level semantic information in the feature extraction stage. GoogleNet's Inception is used to reduce the parameter increase by reducing the kernel size and increasing the model depth. The segmentation algorithm is verified on the ISIC2018 melanoma dataset. Experimental results show that the Intersection over Union (IoU), sensitivity, precision, Dice coefficient, and accuracy are 79.54%, 86.29%, 90.92%, 84.39%, and 94.83%, respectively. Therefore, the proposed algorithm can effectively improve the melanoma segmentation performance.