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Automatic classification of white blood cells using deep features based convolutional neural network

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Abstract

White blood cells (WBCs) are widely presented in human body which plays an important role in the human body immune system. Recently, the incidence of blood diseases related to WBC increases in the human body. The optimal WBC count given useful information for the diagnosis of the blood disease and it has become a popular field of research applications. Hence, in this paper, Deep Features based Convolutional Neural Network (DFCNN) is developed to identify the count of WBC from the image database. The proposed method is working with three phases such as feature extraction, feature selection and classification phases. In the feature extraction phase, the Combined CNN structure is designed which combination of AlexNet, GoogLeNet and ResNet-50 respectively. The combined CNN architecture is utilized to extract 3000 essential features from the images database. In the feature selection phase, hybrid Mayfly Algorithm with Particle Swarm Optimization (HMA-PSO) is designed for selecting the essential feature from the feature sets. In the HMA-PSO algorithm, the velocity updating of mayfly is achieved with the help of PSO algorithm. The selected features are sent to the proposed classifier which named as Recurrent Neural Network- Long Short-Term Memory (RNN-LSTM). This classifier is utilized to classify the WBC types such as Neutrophils, Eosinophils, Monocytes and Lymphocytes respectively. The proposed method is implemented in MATLAB and performances are evaluated by statistical measurements such as accuracy, precision, recall, specificity and F_Measure. The proposed method is compared with the existing methods such as MA-RNN and PSO-RNN respectively. The proposed methodology has been achieved best performance metrics such as recall: 0.98, precision: 0.9 and accuracy; 0.97.

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The already existing algorithms data used to support the findings of this study have not been made available.

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Authors and Affiliations

  1. Department of Computer Science and Applications, SRMIST, Chennai, India

    A. Meenakshi

  2. Department of Computer Science, SRM Institute of Science and Technology, Chennai, India

    J. Anitha Ruth

  3. Department of Humanities and Sciences, Sri Sai Ram Engineering College, Chennai, India

    V. R. Kanagavalli

  4. Department of Computer Science and Engineering, Sri Sairam Engineering College, Chennai, India

    R. Uma

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  1. A. Meenakshi
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  3. V. R. Kanagavalli
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  4. R. Uma
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Correspondence to A. Meenakshi.

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Meenakshi, A., Ruth, J.A., Kanagavalli, V.R. et al. Automatic classification of white blood cells using deep features based convolutional neural network. Multimed Tools Appl 81, 30121–30142 (2022). https://doi.org/10.1007/s11042-022-12539-2

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  • DOI: https://doi.org/10.1007/s11042-022-12539-2

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