Objective To apply the image recognition method based on distance graph convolutional network to the image processing of malaria-infected erythrocytes for realizing the multi-stage recognition of malaria and improving the diagnostic efficiency of malaria.Methods A multi-stage malaria recognition model based on distance graph convolutional network was proposed.A radial basis function was firstly added in KNN graph construction algorithm to construct adjacency matrix and assign weights to the nearest-neighbor nodes according to the similarity between nodes,so as to weaken the effects of the distant nearest-neighbor nodes on the central node.Then,attention mechanism was introduced to update adjacency matrix dynamically in the graph convolutional network for making the model pay attention to near-neighbor nodes with higher similarity,and finally the multi-stage image recognition of malaria-infected erythrocytes was completed.Results Validated on the Malaria-MIT dataset,the experimental results show that compared with original model,the proposed method improved accuracy,precision,recall rate and F1-score to 96.18%,96.23%,96.18%and 96.18%,respectively.Conclusion The proposed approach can effectively accomplish the task of multi-stage image recognition of malaria-infected erythrocytes.

Objective To apply the image recognition method based on distance graph convolutional network to the image processing of malaria-infected erythrocytes for realizing the multi-stage recognition of malaria and improving the diagnostic efficiency of malaria.Methods A multi-stage malaria recognition model based on distance graph convolutional network was proposed.A radial basis function was firstly added in KNN graph construction algorithm to construct adjacency matrix and assign weights to the nearest-neighbor nodes according to the similarity between nodes,so as to weaken the effects of the distant nearest-neighbor nodes on the central node.Then,attention mechanism was introduced to update adjacency matrix dynamically in the graph convolutional network for making the model pay attention to near-neighbor nodes with higher similarity,and finally the multi-stage image recognition of malaria-infected erythrocytes was completed.Results Validated on the Malaria-MIT dataset,the experimental results show that compared with original model,the proposed method improved accuracy,precision,recall rate and F1-score to 96.18%,96.23%,96.18%and 96.18%,respectively.Conclusion The proposed approach can effectively accomplish the task of multi-stage image recognition of malaria-infected erythrocytes.

To address the challenges of blurred boundaries and feature loss in the segmentation of small liver tumors in CT images,a novel model named BounDer-Net is proposed based on an improved ResUNet architecture.By constructing a Transformer-based dynamic multi-scale encoder and introducing a channel-spatial dual-path attention mechanism,the model can focus on tumor features across multiple dimensions.Additionally,the model adopts boundary-sensitive dynamic feature fusion strategy which effectively captures the heterogeneous features of tumors.BounDer-Net model firstly generates initial feature maps through low-level feature extraction,then inputs the features into a Transformer-based dynamic multi-scale encoder for extracting multi-level features,and finally restores spatial details via a decoder and improves the segmentation accuracy of small tumor boundaries using a boundary enhancement module.Experimental results on the LiTS2017 dataset show that BounDer-Net model achieves a Dice similarity coefficient of 94.64%,a mean intersection over union of 92.34%,and a Hausdorff distance of 0.35 mm,significantly outperforming existing methods.This study provides a reliable solution for the automatic diagnosis of small tumors in liver CT images.

To address the challenges of blurred boundaries and feature loss in the segmentation of small liver tumors in CT images,a novel model named BounDer-Net is proposed based on an improved ResUNet architecture.By constructing a Transformer-based dynamic multi-scale encoder and introducing a channel-spatial dual-path attention mechanism,the model can focus on tumor features across multiple dimensions.Additionally,the model adopts boundary-sensitive dynamic feature fusion strategy which effectively captures the heterogeneous features of tumors.BounDer-Net model firstly generates initial feature maps through low-level feature extraction,then inputs the features into a Transformer-based dynamic multi-scale encoder for extracting multi-level features,and finally restores spatial details via a decoder and improves the segmentation accuracy of small tumor boundaries using a boundary enhancement module.Experimental results on the LiTS2017 dataset show that BounDer-Net model achieves a Dice similarity coefficient of 94.64%,a mean intersection over union of 92.34%,and a Hausdorff distance of 0.35 mm,significantly outperforming existing methods.This study provides a reliable solution for the automatic diagnosis of small tumors in liver CT images.