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Classification of white blood cells with SVM by selecting SqueezeNet and LIME properties by mRMR method

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Abstract

White blood cells, which have an important role in the human immune system, protect the body against various viruses, harmful bacteria and infections. If there are not enough white blood cells in the blood, it results in leukopenia. When white blood cells are examined under a microscope, their change in structure and shape indicates some diseases. When experts clinically examine these images, various problems may arise due to individual misinterpretations. In this study, a diagnostic model based on convolutional neural network (CNN), local interpretable model agnostic annotations (LIME) and minimum redundancy maximum association (mRMR) methods is proposed for the detection of four different white blood cells. For this purpose, firstly, after the deep features of the images were extracted with SqueezeNet CNN, the important regions of the images for classification purposes were determined by the LIME method and the distinctive features of the images were obtained. The features obtained with the SqueezeNet CNN model were also obtained with the mRMR feature selection algorithm. Various feature sets obtained by combining the features obtained with the LIME algorithm are classified with support vector machines. As a result, the accuracy rate of the proposed model for the diagnosis of white blood cells was 95.88%. Selecting the SqueezeNet features with the mRMR method and supporting them with LIME features positively affected the performance results.

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  1. Department of Computer Technology, Agri Ibrahim Cecen University, Agri, Turkey

    Erdal Başaran

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  1. Erdal Başaran
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Correspondence to Erdal Başaran.

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Başaran, E. Classification of white blood cells with SVM by selecting SqueezeNet and LIME properties by mRMR method. SIViP 16, 1821–1829 (2022). https://doi.org/10.1007/s11760-022-02141-2

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  • DOI: https://doi.org/10.1007/s11760-022-02141-2

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