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White Blood Cell Classification Using Genetic Algorithm–Enhanced Deep Convolutional Neural Networks

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Advances in Artificial Intelligence and Applied Cognitive Computing

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Abstract

The amount of white blood cells in the blood is of great importance for disease diagnosis. White blood cells include five main classes (eosinophils, lymphocytes, monocytes, neutrophils, basophils), each of which is an important indicator for specific diseases. Deep learning models have been developed to successfully classify the different white blood cell types. The most prominent deep learning models in image classification are deep convolutional neural network (D-CNN) models. A key challenge when solving a problem using deep learning is identifying and setting the hyperparameters for the algorithm. Mostly, these hyperparameters are set manually based on experience. In this study, a new model of deep convolutional neural network is proposed for the classification of four white blood cells types. In this model, the hyperparameters are self-optimized by a genetic algorithm which provides significant improvement in the model. For the verification of the proposed model, four types of white blood cells available from the Kaggle data series were studied. The number of white blood cell images are about 12,000 and are split for training and test sets as 80% and 20%, respectively. When the proposed model was applied to the Kaggle white blood cell data set, the four white blood cell types in the sample data set were classified with high accuracy. The genetic algorithm (GA)–enhanced D-CNN model produced above 93% classification accuracy for the test set demonstrating the success of the proposed enhancement to the D-CNN model with GA. Comparatively, D-CNN models without GA optimization, such as Inception V3 model, produced 84% accuracy, and ResNet-50 model achieved 88% accuracy.

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Acknowledgments

Images of human white blood cells were selected from publicly available databases without any identifying information on any individuals. This work has been supported in part by Ondokuz Mayis University Research Center.

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Authors and Affiliations

  1. Ondokuz Mayis University, Samsun, Turkey

    Omer Sevinc

  2. Texas A&M University-Corpus Christi, Corpus Christi, TX, USA

    Mehrube Mehrubeoglu

  3. Ankara University, Computer Engineering, Ankara, Turkey

    Mehmet S. Guzel & Iman Askerzade

Authors
  1. Omer Sevinc
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  2. Mehrube Mehrubeoglu
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  3. Mehmet S. Guzel
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  4. Iman Askerzade
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Corresponding author

Correspondence to Omer Sevinc .

Editor information

Editors and Affiliations

  1. Department of Computer Science, University of Georgia, Athens, GA, USA

    Hamid R. Arabnia

  2. Department of Electrical and Computer Engineering, University of Manitoba, Winnipeg, MB, Canada

    Ken Ferens

  3. Business Administration, University of Oviedo, Oviedo, Asturias, Spain

    David de la Fuente

  4. Institute of Informatics Problems, The Russian Academy of Sciences, Moscow, Russia

    Elena B. Kozerenko

  5. Technology and Information systems, Universidad de Castilla La Mancha, Ciudad Real, Ciudad Real, Spain

    José Angel Olivas Varela

  6. Facultad de Informática - CIC PBA, Universidad Nacional de La Plata, La Plata, Argentina

    Fernando G. Tinetti

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Sevinc, O., Mehrubeoglu, M., Guzel, M.S., Askerzade, I. (2021). White Blood Cell Classification Using Genetic Algorithm–Enhanced Deep Convolutional Neural Networks. In: Arabnia, H.R., Ferens, K., de la Fuente, D., Kozerenko, E.B., Olivas Varela, J.A., Tinetti, F.G. (eds) Advances in Artificial Intelligence and Applied Cognitive Computing. Transactions on Computational Science and Computational Intelligence. Springer, Cham. https://doi.org/10.1007/978-3-030-70296-0_3

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  • DOI: https://doi.org/10.1007/978-3-030-70296-0_3

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