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DMDLK-Net: A dynamic multi-scale feature fusion network with deformable large kernel for medical segmentation
- 1 Nankai University
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Abstract
Skin disease image segmentation is a crucial component of computer-aided diagnosis, providing precise localization and delineation of lesions that enhance diagnostic accuracy and efficiency. Despite significant advancements in convolutional neural networks (CNNs), there remains substantial room for improvement in segmentation performance due to the diverse and complex nature of skin lesions. In this study, we propose DMDLK-Net, a dynamic multi-scale feature fusion network with deformable large kernels, specifically designed to address the challenges in skin disease segmentation. Our network incorporates a Dynamic Deformable Large Kernel (DDLK) module and a Dynamic Multi-Scale Feature Fusion (DMFF) module, enhancing the model's ability to capture intricate lesion features. We present the performance of DMDLK-Net on the ISIC-2018 dataset, highlighting its promising results. Key contributions of this work include the innovative use of deformable large kernels for adaptive feature extraction and the introduction of dynamic multi-scale fusion to balance local and global information. Our experimental results confirm the effectiveness of DMDLK-Net in delivering high-precision segmentation, thus providing a reliable tool for clinical applications.
Keywords
skin lesion segmentation, attention mechanism, deformable large kernel, multi-scale information, dynamic feature fusion.
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Cite this article
Wei,B. (2024). DMDLK-Net: A dynamic multi-scale feature fusion network with deformable large kernel for medical segmentation. Applied and Computational Engineering,95,79-85.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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Volume title: Proceedings of the 6th International Conference on Computing and Data Science
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