Automated Urine Cell Image Classification Model Using Chaotic Mixer Deep Feature Extraction
Article
Erten, Mehmet, Tuncer, Ilknur, Barua, Prabal D., Yildirim, Kubra, Dogan, Sengul, Tuncer, Turker, Tan, Ru-San, Fujita, Hamido and Acharya, U. Rajendra. 2023. "Automated Urine Cell Image Classification Model Using Chaotic Mixer Deep Feature Extraction." Journal of Digital Imaging. 36 (4), pp. 1675-1686. https://doi.org/10.1007/s10278-023-00827-8
| Article Title | Automated Urine Cell Image Classification Model Using Chaotic Mixer Deep Feature Extraction |
|---|---|
| ERA Journal ID | 35211 |
| Article Category | Article |
| Authors | Erten, Mehmet, Tuncer, Ilknur, Barua, Prabal D., Yildirim, Kubra, Dogan, Sengul, Tuncer, Turker, Tan, Ru-San, Fujita, Hamido and Acharya, U. Rajendra |
| Journal Title | Journal of Digital Imaging |
| Journal Citation | 36 (4), pp. 1675-1686 |
| Number of Pages | 12 |
| Year | 2023 |
| Publisher | Springer |
| Place of Publication | United States |
| ISSN | 0897-1889 |
| 1618-727X | |
| Digital Object Identifier (DOI) | https://doi.org/10.1007/s10278-023-00827-8 |
| Web Address (URL) | https://link.springer.com/article/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. |
| Keywords | Biomedical image classification; Chaotic mixer deep feature extraction; Urine cell classifcation ; Image classifcation; Feature engineering; Urine analysis; Urine sediment |
| ANZSRC Field of Research 2020 | 400306. Computational physiology |
| Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
| Byline Affiliations | Malatya Training and Research Hospital, Turkiye |
| Interior Ministry, Turkiye | |
| School of Business | |
| Cogninet Australia, Australia | |
| University of Technology Sydney | |
| Australian International Institute of Higher Education, Australia | |
| University of New England | |
| Taylor’s University, Malaysia | |
| SRM Institute of Science and Technology, India | |
| Kumamoto University, Japan | |
| University of Sydney | |
| Firat University Hospital, Turkey | |
| National Heart Centre, Singapore | |
| Duke-NUS Medical School, Singapore | |
| HUTECH University of Technology, Vietnam | |
| University of Granada, Spain | |
| Iwate Prefectural University, Japan | |
| School of Mathematics, Physics and Computing |
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