Medical image segmentation method based on multi-feature interaction and fusion over cloud computing

Medical image segmentation is a crucial task in computer-aided diagnosis. While deep learning has significantly improved this field, relying solely on local computing power makes it challenging to achieve real-time segmentation results. Furthermore, traditional convolutional neural networks (CNNs) lack the ability to extract global features. To address these issues, this paper proposes a cloud-based medical image segmentation method that leverages multi -feature extraction and interactive fusion. Specifically, this method employs cloud computing to process a large number of medical images and overcome local computing power limitations. It also combines Transformer and CNNs to extract global and local features, respectively, and introduces an interactive fusion attention module to improve segmentation accuracy. The proposed approach is validated on multiple medical image datasets, and experimental results demonstrate its effectiveness and progress.

Automated summary

Medical image segmentation is a crucial task in computer-aided diagnosis, and this paper proposes a cloud-based method that leverages multi-feature extraction and interactive fusion to address the limitations of local computing power and the inability of traditional CNNs to extract global features. The proposed approach combines Transformer and CNNs and introduces an interactive fusion attention module to improve segmentation accuracy. Experimental results on multiple medical image datasets validate the effectiveness and progress of the proposed method.