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An Effective Convolutional Neural Network for Classifying Red Blood Cells in Malaria Diseases

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Abstract

Malaria is one of the epidemics that can cause human death. Accurate and rapid diagnosis of malaria is important for treatment. Due to the limited number of data and human factors, the prediction performance and reliability of traditional classification methods are often affected. In this study, we propose an efficient and novel classification network named Attentive Dense Circular Net (ADCN) which based on Convolutional Neural Networks (CNN). The ADCN is inspired by the ideas of residual and dense networks and combines with the attention mechanism. We train and evaluate our proposed model on a publicly available red blood cells (RBC) dataset and compare ADCN with several well-established CNN models. Compared to other best performing CNN model in our experiments, ADCN shows superiority in all performance criteria such as accuracy (97.47% vs 94.61%), sensitivity (97.86% vs 95.20%) and specificity (97.07% vs 92.87%). Finally, we discuss the obtained results and analyze the difficulties of RBCs classification.

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Notes

  1. https://ceb.nlm.nih.gov/repositories/malaria-datasets/

  2. https://keras.io/applications/

  3. https://github.com/titu1994/Keras-DualPathNetworks/

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Acknowledgements

This work was supported by the National Natural Science Foundation of China under grants No. 61772557, No. 61772552; the 111 Project (No. B18059); and the Hunan Provincial Science and Technology Program (2018 WK4001). This paper was recommended by CBC2019.

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Authors and Affiliations

  1. School of Computer Science and Engineering, Central South University, Changsha, 410083, People’s Republic of China

    Quan Quan, Jianxin Wang & Liangliang Liu

  2. Department of Network Center, Pingdingshan University, Pingdingshan, 467000, People’s Republic of China

    Liangliang Liu

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  1. Quan Quan
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Quan, Q., Wang, J. & Liu, L. An Effective Convolutional Neural Network for Classifying Red Blood Cells in Malaria Diseases. Interdiscip Sci Comput Life Sci 12, 217–225 (2020). https://doi.org/10.1007/s12539-020-00367-7

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