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Automated Urine Cell Image Classification Model Using Chaotic Mixer Deep Feature Extraction

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Abstract

Microscopic examination of urinary sediments is a common laboratory procedure. Automated image-based classification of urinary sediments can reduce analysis time and costs. Inspired by cryptographic mixing protocols and computer vision, we developed an image classification model that combines a novel Arnold Cat Map (ACM)- and fixed-size patch-based mixer algorithm with transfer learning for deep feature extraction. Our study dataset comprised 6,687 urinary sediment images belonging to seven classes: Cast, Crystal, Epithelia, Epithelial nuclei, Erythrocyte, Leukocyte, and Mycete. The developed model consists of four layers: (1) an ACM-based mixer to generate mixed images from resized 224 × 224 input images using fixed-size 16 × 16 patches; (2) DenseNet201 pre-trained on ImageNet1K to extract 1,920 features from each raw input image, and its six corresponding mixed images were concatenated to form a final feature vector of length 13,440; (3) iterative neighborhood component analysis to select the most discriminative feature vector of optimal length 342, determined using a k-nearest neighbor (kNN)-based loss function calculator; and (4) shallow kNN-based classification with ten-fold cross-validation. Our model achieved 98.52% overall accuracy for seven-class classification, outperforming published models for urinary cell and sediment analysis. We demonstrated the feasibility and accuracy of deep feature engineering using an ACM-based mixer algorithm for image preprocessing combined with pre-trained DenseNet201 for feature extraction. The classification model was both demonstrably accurate and computationally lightweight, making it ready for implementation in real-world image-based urine sediment analysis applications.

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Data Availability

The data used in this study were downloaded from [6,7,8].

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Author information

Authors and Affiliations

  1. Department of Medical Biochemistry, Malatya Training and Research Hospital, Malatya, Türkiye

    Mehmet Erten

  2. Elazig Governorship, Interior Ministry, Elazig, Türkiye

    Ilknur Tuncer

  3. Cogninet Australia, Sydney, NSW, 2010, Australia

    Prabal D. Barua

  4. School of Business (Information System), University of Southern Queensland, Toowoomba, Australia

    Prabal D. Barua

  5. Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, NSW, 2007, Australia

    Prabal D. Barua

  6. Australian International Institute of Higher Education, Sydney, NSW, 2000, Australia

    Prabal D. Barua

  7. School of Science and Technology, University of New England, Armidale, Australia

    Prabal D. Barua

  8. School of Biosciences, Taylor’s University, Subang Jaya, Malaysia

    Prabal D. Barua

  9. School of Computing, SRM Institute of Science and Technology, Chennai, India

    Prabal D. Barua

  10. School of Science and Technology, Kumamoto University, Kumamoto, Japan

    Prabal D. Barua

  11. Sydney School of Education and Social Work, University of Sydney, Sydney, Australia

    Prabal D. Barua

  12. Department of Digital Forensics Engineering, Technology Faculty, Firat University, Elazig, Türkiye

    Kubra Yildirim, Sengul Dogan & Turker Tuncer

  13. Department of Cardiology, National Heart Centre Singapore, Singapore, Singapore

    Ru-San Tan

  14. Duke-NUS Medical School, Singapore, Singapore

    Ru-San Tan

  15. Faculty of Information Technology, HUTECH University, Ho Chi Minh City, Vietnam

    Hamido Fujita

  16. Andalusian Research Institute in Data Science and Computational Intelligence, University of Granada, Granada, Spain

    Hamido Fujita

  17. Regional Research Center, Iwate Prefectural University, Iwate, Japan

    Hamido Fujita

  18. School of Mathematics, Physics and Computing, University of Southern Queensland, Springfield, Australia

    U. Rajendra Acharya

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  1. Mehmet Erten
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  2. Ilknur Tuncer
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Contributions

Conceptualization: ME, IT, PDB, KY, SD, TT, RST, HF, URA; formal analysis: ME, IT, PDB, KY; investigation: ME, IT, PDB, KY; methodology: ME, IT, PDB, KY, SD, TT, RST, HF, URA; software: SD, TT; project administration: URA; resources: IT, PDB; supervision: URA; validation: ME, IT, PDB, KY, SD, TT, RST, HF, URA; visualization: ME, IT, PDB, KY, SD, TT; writing—original draft: ME, IT, PDB, KY, SD, TT, RST, HF, URA; writing—review and editing: ME, IT, PDB, KY, SD, TT, RST, HF, URA; all authors have read and agreed to the published version of the manuscript.

Corresponding author

Correspondence to Sengul Dogan.

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Erten, M., Tuncer, I., Barua, P.D. et al. Automated Urine Cell Image Classification Model Using Chaotic Mixer Deep Feature Extraction. J Digit Imaging 36, 1675–1686 (2023). https://doi.org/10.1007/s10278-023-00827-8

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  • DOI: https://doi.org/10.1007/s10278-023-00827-8

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