Early detection of skin cancer relies on precise segmentation of dermoscopic images of skin lesions. However, this task is challenging due to the irregular shape of the lesion, the lack of sharp borders, and the presence of artefacts such as marker colours and hair follicles. Recent methods for melanoma segmentation are U-Nets and fully connected networks (FCNs). As the depth of these neural network models increases, they can face issues like the vanishing gradient problem and parameter redundancy, potentially leading to a decrease in the Jaccard index of the segmentation model. In this study, we introduced a novel network named TESL-Net for the segmentation of skin lesions. The proposed TESL-Net involves a hybrid network that combines the local features of a CNN encoder-decoder architecture with long-range and temporal dependencies using bi-convolutional long-short-term memory (Bi-ConvLSTM) networks and a Swin transformer. This enables the model to account for the uncertainty of segmentation over time and capture contextual channel relationships in the data. We evaluated the efficacy of TESL-Net in three commonly used datasets (ISIC 2016, ISIC 2017, and ISIC 2018) for the segmentation of skin lesions. The proposed TESL-Net achieves state-of-the-art performance, as evidenced by a significantly elevated Jaccard index demonstrated by empirical results.

本研究解决了皮肤癌早期检测中由于病变形状不规则、边界不清晰以及伪影影响而导致的精准分割问题。该论文提出的TESL-Net通过结合卷积神经网络（CNN）与双卷积长短期记忆网络（Bi-ConvLSTM）和Swin变压器，捕捉长期依赖关系和上下文通道关系，从而提高了分割的准确性。在ISIC 2016、ISIC 2017和ISIC 2018数据集上的评价显示，TESL-Net在分割任务中达到了先进的性能，显著提高了Jaccard指数。

TESL-Net：一种增强的变压器卷积神经网络用于精准皮肤病变分割