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Malaria cell image classification by explainable artificial intelligence

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Abstract

Malaria is one of the most common diseases in the world. It is caused by Plasmodium parasites and spreads among humans by the bite of female Anopheles mosquitoes. Only in 2019, children under the age of five accounted for 67% of all malaria deaths. The diagnosing process of malaria can be error-prone due to an inexperienced pathologist and may take a longer time. In this paper, we have presented a malaria detection framework based on wavelet packet 2d, Convolutional Neural Network.

(CNN), and Whale Optimization Algorithm (WOA). We have extracted a global feature set using the wavelet packet 2D and CNN. Further, we removed noisy features and selected an effective feature using WOA and XGBoost. The selected feature set is almost half of the initial feature set. Additionally, we have used SHapley Additive exPlanations (SHAP) to interpret the trained model and assess the significance of each feature. Based on the selected feature set, the XGBoost algorithm provides accuracy, precision, recall, and F1 score of 94. 78%, 94. 39%, 95. 21%, and 94. 80% respectively.

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Data availability

https://lhncbc.nlm.nih.gov/LHC-downloads/downloads.html#malariadatasets

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  1. Electronics and Communication Engineering Discipline, Khulna University, Khulna, 9208, Bangladesh

    Md. Johir Raihan & Abdullah-Al Nahid

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  1. Md. Johir Raihan
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Raihan, M., Nahid, AA. Malaria cell image classification by explainable artificial intelligence. Health Technol. 12, 47–58 (2022). https://doi.org/10.1007/s12553-021-00620-z

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