# An Improved Multi-Scale Feature Fusion for Skin Lesion Segmentation
## 6 Conclusions  
This paper proposes a fusion multi-scale convolutional neural network based on the UNet3+ structure named REDAUNet. Dermoscopy images are different from the image segmentation of organs such as the brain and lungs, which have clear boundaries and fixed locations. Most skin lesions have fuzzy boundaries with the surrounding area, and especially when the shape of some lesions is complicated, it is difficult to observe the boundary position with human eyes. To address this problem, by introducing a newly designed cross-residual block to replace the simple stacked convolutional blocks in the original encoder, this module not only enhances the ability to extract deep semantic information, but also preserves shallow feature information. Simultaneously, due to the huge variation in the size of skin lesions, some dermoscopy images have less than one-tenth of the lesion area and too many irrelevant areas, an attention mechanism is added to assign a different weight coefficient to each channel so as to enhance relevant feature information and suppress irrelevant information. The DenseASPP module uses feature maps with various expansion rates to obtain multi-scale information. Compared with previous models, the results show that the proposed model has significantly improved the segmentation of skin lesions and has good anti-interference and generalization performance, particularly in terms of Dice and mIoU metrics, which increased by 2.01% and 2.68% with our model. Compared with the UNeXt model, our proposed model has a slight improvement in the four indicators, and the improvement in mIoU is 2.44%. Therefore, the CAD system based on this model has a certain reference value in the medical imaging field. Further research on the model's shortcomings is necessary. Currently, there is a wide range of research and applications for lightweight networks. By replacing the convolutional layer of the model with a separable convolution, the number of parameters and training time of the model will be significantly reduced, but the effect on the model performance needs to be further investigated. In addition, due to the variety of skin lesions, a single model segmentation has low fault tolerance and suffers from excessive bias or variance. It is also helpful to improve the model performance if the results of multiple models are integrated using an integrated model, thus reducing the model error. In the continuous development and innovation of technology, it has become possible to apply research findings to mobile applications. By integrating this research into mobile applications, it is expected to further enhance user experience and meet people's growing needs. In the future, we will further explore the application of other modules and algorithms, strengthen the research on skin lesion images, further enhance the performance and accuracy of semantic segmentation, and promote it to more medical image segmentation tasks.  
Funding acquisition, X.Z.; resources, X.Z.; supervision, X.Z., Y.L. and Z.X.; writing--original draft, L.L.; writing--review and editing, L.L., X.Z., Y.L. and Z.X. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Innovation and Entrepreneurship Training Program of the University of Science and Technology Liaoning (grant no. X202210146004).  
**Informed Consent Statement**: Not applicable.  
Data Availability Statement: The datasets used in this paper are public datasets. The ISIC 2017 could be found 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)) (accessed on 25 March 2023). The ISIC2018 could be found 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)) (accessed on 25 March 2023).  
Conflicts of Interest: The authors declare no conflict of interest.