Affiliations 

  • 1 Department of Medical Biochemistry, Malatya Training and Research Hospital, Malatya, Türkiye
  • 2 Elazig Governorship, Interior Ministry, Elazig, Türkiye
  • 3 Cogninet Australia, Sydney, NSW, 2010, Australia
  • 4 Department of Digital Forensics Engineering, Technology Faculty, Firat University, Elazig, Türkiye
  • 5 Department of Digital Forensics Engineering, Technology Faculty, Firat University, Elazig, Türkiye. sdogan@firat.edu.tr
  • 6 Department of Cardiology, National Heart Centre Singapore, Singapore, Singapore
  • 7 Faculty of Information Technology, HUTECH University, Ho Chi Minh City, Vietnam
  • 8 School of Mathematics, Physics and Computing, University of Southern Queensland, Springfield, Australia
J Digit Imaging, 2023 Aug;36(4):1675-1686.
PMID: 37131063 DOI: 10.1007/s10278-023-00827-8

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.

* Title and MeSH Headings from MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine.