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Malaria infected erythrocyte classification based on a hybrid classifier using microscopic images of thin blood smear

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Abstract

This paper aims to develop the computer assisted malaria infected erythrocyte classification based on a hybrid classifier. The major issues are feature extraction, optimal feature selection and erythrocytes classification. 54 dimensional features formed by the combination of the proposed features and the existing features have been used to define the feature set. The features such as prediction error, co-occurrence of linear binary pattern, chrominance channel histogram, R-G color channel difference histogram are the newly proposed features in our system. For feature selection, the different techniques have been explored to obtain the optimal feature set. Further, the performance of the different individual classifiers (SVM, k-NN and Naive Bayes) and hybrid classifier, obtained by combining the individual classifiers, is evaluated using the optimal feature set. Using the proposed optimal feature set and hybrid model, better performances (i.e. sensitivity 95.86%, accuracy 98.5%, F-score 93.82%) have been achieved on the collected clinical database. Based on the experimental results it may be concluded that hybrid classifier provides satisfactory results with an improvement in sensitivity (1.09%, 12.04%, 0%), accuracy (0.12%, 1.15%, 1.27%) and F-score (0.7%, 5.77%, 4.61%) as compared to the individual classifiers i.e. SVM, k-NN and Naive Bayes respectively.

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  • 02 March 2017

    An erratum to this article has been published.

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Acknowledgements

This work is supported by the Speech and Image Processing Lab under Department of ECE at National Institute of Technology, Silchar, India.

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Authors and Affiliations

  1. Speech and Image Processing Group, Department of Electronics and Communication Engineering, National Institute of Technology, Silchar, Assam, 788010, India

    Salam Shuleenda Devi, Amarjit Roy, Joyeeta Singha & Rabul Hussain Laskar

  2. Silchar Medical College and Hospital, Silchar, Assam, 788014, India

    Shah Alam Sheikh

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  1. Salam Shuleenda Devi
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  2. Amarjit Roy
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  3. Joyeeta Singha
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Correspondence to Salam Shuleenda Devi.

Additional information

An erratum to this article is available at https://doi.org/10.1007/s11042-017-4490-7.

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Devi, S.S., Roy, A., Singha, J. et al. Malaria infected erythrocyte classification based on a hybrid classifier using microscopic images of thin blood smear. Multimed Tools Appl 77, 631–660 (2018). https://doi.org/10.1007/s11042-016-4264-7

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