## 6 Conclusions  
This study proposes a novel convolutional neural network based on the U-Net architecture, termed MA-DenseUNet, which integrates dense convolutional blocks, a multi-scale attention mechanism, and bidirectional LSTM units to enhance the accuracy and robustness of medical image segmentation tasks. Experimental results demonstrate that each of the proposed components contributes significantly to overall performance improvement. The dense convolutional blocks strengthen the representation of deep semantic features and effectively mitigate gradient vanishing and feature redundancy. To address the considerable variation in lesion sizes--ranging from large regions occupying most of the image to small lesions comprising less than one-tenth of the area--a multi-scale attention mechanism is incorporated to guide the model's focus toward critical lesion regions while suppressing background noise. Furthermore, the integration of the BDC-LSTM module enhances the modeling of spatial contextual information, showing superior structural awareness and regional consistency, particularly in cases of blurred boundaries and unclear textures. In addition, a multi-scale generative adversarial network (GAN) is employed for joint training, leveraging multi-scale discriminators to refine segmentation details and compensate for the local structure insensitivity of traditional loss functions.  
Comprehensive experimental evaluations reveal that MA-DenseUNet achieves outstanding performance across multiple benchmark medical image segmentation datasets, significantly outperforming existing state-of-the-art models, thereby validating its strong segmentation capability and generalizability. Nevertheless, the model remains relatively complex in structure and demands substantial computational resources. Its performance on extremely small lesions or low-contrast images still presents challenges. Future work will focus on incorporating lightweight architectural designs to reduce model complexity, and exploring self-supervised and few-shot learning strategies to further improve the model's applicability and clinical deployment potential.  
Conceptualization, Y.Y. and X.C.; methodology, X.C.; software, W.H.; validation, Y.Y. and Y.K.; formal analysis, W.H.; investigation, W.H.; resources, W.H.; data curation, Y.K.; writing--original draft preparation, X.C.; writing--review and editing, Y.Y.; visualization, Y.Y.; supervision, Y.Y.; project administration, W.H.; funding acquisition, Y.Y. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Jilin Provincial Science and Technology Development Plan \"Research on Multi-Target Classification Techniques Incorporating Efficient Channel Attention Mechanisms\", approval number YDZJ202401350 ZYTS.  
Not applicable.  
The datasets used in this study are public datasets. The PH\({}^{2}\) dataset can be accessed at the following URL: [[https://www.fc.up.pt/addi/project.html](https://www.fc.up.pt/addi/project.html)]([https://www.fc.up.pt/addi/project.html](https://www.fc.up.pt/addi/project.html)), the ISIC 2017 dataset can be accessed at [[https://challenge.isic-archive.com/data/#2017](https://challenge.isic-archive.com/data/#2017)]([https://challenge.isic-archive.com/data/#2017](https://challenge.isic-archive.com/data/#2017)), and the ISIC 2018 dataset can be accessed at [[https://challenge.isic-archive.com/data/#2018](https://challenge.isic-archive.com/data/#2018)]([https://challenge.isic-archive.com/data/#2018](https://challenge.isic-archive.com/data/#2018)).