## 1 Introduction
[20] further proposed an Efficient-GAN framework based on U-Net, employing a composite scaling encoder based on Squeeze-and-Excitation to capture dense features and generate segmentation results, while using adversarial learning to distinguish between real and synthetic labels, ultimately improving the boundary segmentation accuracy in lesion regions. However, existing deep learning methods still face certain limitations. For instance, when processing skin lesion images, traditional U-Net architectures may experience feature loss during the up-sampling and down-sampling processes of the encoder-decoder, which affects segmentation accuracy. Moreover, existing methods often fail to fully account for the unique characteristics of skin lesion images, such as variations in the size and shape of lesion regions, hair occlusion, bubble interference, and uneven coloration. To overcome these challenges, we propose a novel skin lesion segmentation framework that synergistically integrates a U-Net architecture incorporating dense convolutional blocks, attention mechanisms, and Long Short-Term Memory (LSTM) module with the discriminative power of a multi-scale Generative Adversarial Network (GAN). The proposed method achieves dual improvements in both segmentation precision and feature discriminability for diverse lesion patterns, demonstrating state-of-the-art performance across multiple benchmark dermatoscopic image datasets. The principal contributions of this work include:  
1. Improvement of U-Net Architecture: We have made significant improvements to the traditional U-Net architecture by enhancing the encoder-decoder up-sampling modules, incorporating attention mechanisms in the skip connections, and integratinggenerative adversarial training. These modifications strengthen the model's ability to recognize and segment complex skin lesion features. 2. Integration of Attention Mechanism: The attention mechanism is introduced into the model, a strategy that significantly improves the model's ability to segment skin lesions and normal skin areas, as well as distinguish between common and rare types of lesions, by learning the features of the lesion regions in a batch manner. 3. Addressing Feature Loss Issue: By incorporating the Bidirectional Convolutional Long Short-Term Memory (BDC-LSTM) module, which combines Long Short-Term Memory (LSTM) and Convolutional Neural Networks (CNNs), we effectively address issues such as gradient vanishing or explosion during training. This approach helps retain crucial feature information and focuses on solving the feature loss problem commonly encountered in the traditional U-Net architecture during the up-sampling and down-sampling processes. 4. Extensive Experimental Validation: We conducted extensive experiments on multiple skin lesion image datasets to validate the effectiveness of our proposed method and its applicability in recognizing different types of skin lesions. Additionally, we assessed the generalizability of the model across other datasets. The following sections of this paper will provide a detailed description of our method, including the design of the model architecture, the implementation of generative adversarial training, and the experimental results and analyses on various datasets.