MA-DenseUNet: A Skin Lesion Segmentation Method Based on Multi-Scale Attention and Bidirectional LSTM - Wenbo Huang; Xudong Cai; Yang Yan; Yufeng Kang (Applied Sciences, 2025)
Skin diseases are common medical conditions, and early detection significantly contributes to improved cure rates. To address the challenges posed by complex lesion morphology, indistinct boundaries, and image artifacts, this paper proposes a skin lesion segmentation method based on multi-scale attention and bidirectional long short-term memory (Bi-LSTM). Built upon the U-Net architecture, the proposed model enhances the encoder with dense convolutions and an adaptive feature fusion module to strengthen feature extraction and multi-scale information integration. Furthermore, it incorporates both channel and spatial attention mechanisms along with temporal modeling to improve boundary delineation and segmentation accuracy. A generative adversarial network (GAN) is also introduced to refine the segmentation output and boost generalization performance. Experimental results on the ISIC2017 dataset demonstrate that the method achieves an accuracy of 0.950, a Dice coefficient of 0.902, and a mean Intersection over Union (mIoU) of 0.865. These results indicate that the proposed approach effectively improves lesion segmentation performance and offers valuable support for computer-aided diagnosis of skin diseases.