CACDU-Net: A Novel DoubleU-Net Based Semantic Segmentation Model for Skin Lesions Detection in Images

Skin lesion segmentation is a critical task in the field of dermatology as it can aid in the early detection and diagnosis of skin diseases. Deep learning techniques have shown great potential in achieving accurate lesion segmentation. With the help of these techniques, the lesion segmentation process can be automated, thus reducing the impact of manual operations and subjective judgments. This aids in improving the work efficiency of medical professionals by saving their time and lowering their corresponding effort, and in enabling better allocation of healthcare resources. This paper proposes a novel CACDU-Net model, based on the DoubleU-Net model, for performing skin lesion segmentation better. For this, firstly, the proposed model adopts a pre-trained ConvNeXt-T as an encoding backbone network to provide rich image features. Secondly, specially designed ConvNeXt Attention Convolutional Blocks (CACB) are utilized by CACDU-Net to refine feature extraction by combining ConvNeXt blocks with multiple attention mechanisms. Thirdly, the proposed model utilizes a specially designed Asymmetric Convolutional Atrous Spatial Pyramid Pooling (ACASPP) module between the encoding and decoding parts, using atrous convolutions at different scales to capture contextual information at different levels. The image segmentation performance of the proposed model is evaluated against existing mainstream models on two skin lesion public datasets, ISIC2018 and PH2, as well as on a private dataset. The obtained results demonstrate that CACDU-Net achieves excellent results, especially based on the two core metrics used for the evaluation of image segmentation, namely the Intersection over Union (IoU) and Dice similarity coefficient (DSC), according to which it surpasses all other models. Moreover, experiments conducted on the PH2 dataset show that CACDU-Net has strong generalization ability.