Image recognition of malaria-infected erythrocytes based on graph convolutional network
10.3969/j.issn.1005-202X.2025.05.008
- VernacularTitle:基于图卷积网络的疟疾感染红细胞图像识别
- Author:
Wei ZHANG
1
;
Xiaoshuang LIU
1
;
Yuzhang MA
1
;
Haochen SHAO
1
Author Information
1. 甘肃中医药大学医学信息工程学院,甘肃 兰州 730000
- Publication Type:Journal Article
- Keywords:
malaria-infected erythrocyte;
image recognition;
graph convolutional network;
deep learning;
medical image processing
- From:
Chinese Journal of Medical Physics
2025;42(5):606-612
- CountryChina
- Language:Chinese
-
Abstract:
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.
