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Neighbor Correlated Graph Convolutional Network for multi-stage malaria parasite recognition

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Abstract

Malaria is a serious and fatal infectious disease and early detection of the patient’s infection severity can effectively curb the outbreak of this infectious disease. Deep learning has been verified to have excellent capability in image classification and disease diagnosis in many challenging tasks, such as cell detection and histological image classification. There exist many deep learning researches on malaria parasite recognition with successful applications, but they mainly focus on binary classification of single Ring stage and red blood cells. The most important disadvantage of them are ignoring other stages of malaria parasites, including Trophozoite, Gametocytes, and Schizont. In this paper, we are the first to study the multi-stage malaria parasite recognition problem, and propose a novel Neighbor Correlated Graph Convolutional Network (NCGCN) for this challenging task. Specifically, NCGCN consists of CNN (Convolutional Neural Network) feature learning, neighbor correlation mining, and graph representation modules. The method firstly extracts CNN representations from each parasite image and then establishes the neighbor correlations among CNN features by combining K-Nearest Neighbor (KNN) and 𝜖-radius graph building algorithms, with operating Graph Convolutional Network (GCN) on CNN features and their correlations. To evaluate the performance of our NCGCN model, we compare it with several advanced existing methods, and our model can reach a high Accuracy of 94.17%, Precision of 94.84%, Recall of 94.17% and F1-score of 94.20%. The comparison with these outstanding methods verifies the NCGCN model has an excellent capability for recognizing multi-stage malaria parasites, which is higher than the compared methods at least 8.67% in Accuracy.

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Authors and Affiliations

  1. School of Management, Hebei University, Baoding, China

    Xiangjie Meng & Yan Ha

  2. College of Mathematics and Information Science, Hebei University, Baoding, China

    Xiangjie Meng

  3. Key Laboratory on High Trusted Information System in Hebei Province, Baoding, China

    Yan Ha & Junfeng Tian

  4. School of Cyber Security and Computer, Hebei University, Baoding, China

    Junfeng Tian

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  1. Xiangjie Meng
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  2. Yan Ha
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  3. Junfeng Tian
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Correspondence to Yan Ha.

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We declare that we have no financial and personal relationships with other people or organizations that can inappropriately influence our work, and there is no professional or other personal interest of any nature or kind in any product, service and/or company that could be construed as influencing the position presented in, or the review of, the manuscript entitles.

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Xiangjie Meng and Yan Ha contributed to this work equally.

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Meng, X., Ha, Y. & Tian, J. Neighbor Correlated Graph Convolutional Network for multi-stage malaria parasite recognition. Multimed Tools Appl 81, 11393–11414 (2022). https://doi.org/10.1007/s11042-022-12098-6

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