## 5 Discussion  
To tackle the inherent challenges of medical image segmentation, including morphological variability and indistinct lesion boundaries, this study introduces MA-DenseUNet--a novel and task-specific segmentation framework designed to enhance lesion delineation in medical imaging. Specifically, a newly designed dense convolutional module is integrated into the encoder to strengthen feature representation and facilitate the extraction of high-level semantic information. The dense connectivity promotes efficient feature propagation across layers, mitigating issues such as gradient vanishing and feature redundancy.  
To further improve the model's attention to critical lesion regions, a multi-scale Convolutional Block Attention Module (CBAM) is incorporated. By jointly leveraging multi-scale channel and spatial attention, the model effectively concentrates on lesion-relevant features while suppressing background noise. Moreover, to address issues related to blurred lesion boundaries and unclear textures, a Bidirectional Convolutional Long Short-Term Memory (BDC-LSTM) module is embedded within the bottleneck layer. This enables the modeling of forward and backward spatial dependencies, enhancing the integration of local and global contextual information and improving segmentation in complex anatomical structures. Furthermore, a multi-scale Generative Adversarial Network (GAN) is introduced to refine segmentation results through adversarial learning. By employing discriminators at multiple scales, the model enforces fine-grained structural realism in the predicted segmentation maps, particularly at lesion boundaries. This adversarial strategy enhances the generator's capacity to reconstruct detailed lesion contours, contributing to more stable and robust segmentation outcomes, especially under challenging background conditions.  
To evaluate the robustness and generalizability of the proposed method, extensive comparisons are conducted with several state-of-the-art models published in recent years. As shown in Table 3, on the PH\({}^{2}\) dataset, MA-DenseUNet achieves improvements of 0.6% in Dice coefficient, 0.42% in specificity, and 1.5% in sensitivity over LCAUnet, along with a 2.4% increase in mean Intersection over Union (mIoU). These results confirm the  
Figure 18: Visual comparison of ablation study results. (**a**) Segmentation result of the baseline DenseUNet; (**b**) segmentation result of DenseUNet with integrated attention module; (**c**) segmentation result of DenseUNet with skip-connected attention module and CBAM; (**d**) segmentation result of DenseUNet with skip-connected attention module, CBAM, and BDC-LSTM; (**e**) segmentation result of the complete network model.  
model's superior capability in accurately localizing skin lesions, particularly by improving recall without compromising precision. Additionally, as illustrated in Tables 4 and 5, MA-DenseUNet consistently outperforms contemporary approaches on the ISIC dataset across all evaluation metrics. The model demonstrates strong adaptability in segmenting various lesion types, especially those characterized by irregular shapes, fuzzy boundaries, and low contrast. These findings substantiate the effectiveness and practical utility of the proposed framework in both controlled experimental settings and real-world clinical scenarios.