Automated Urine Cell Image Classification Model Using Chaotic Mixer Deep Feature Extraction.
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 nove...
| Publicado en: | Journal of Digital Imaging Vol. 36; no. 4; pp. 1675 - 1687 |
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| Autores principales: | , , , , , , , , |
| Formato: | equations & formulas pictorial research tables/charts Journal Article |
| Publicado: |
Springer Nature
Aug2023
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=169808821&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 169808821 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Aug2023 vid: 36 iid: 4 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 169808821 163448584 169808821 169808821 10.1007/s10278-023-00827-8 169808821 ppf: 1675 ppct: 12 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Automated Urine Cell Image Classification Model Using Chaotic Mixer Deep Feature Extraction. aug: au: Erten, Mehmet Tuncer, Ilknur Barua, Prabal D. Yildirim, Kubra Dogan, Sengul Tuncer, Turker Tan, Ru-San Fujita, Hamido Acharya, U. Rajendra affil: Department of Medical Biochemistry, Malatya Training and Research Hospital, Malatya, Türkiye sug: subj: Algorithms Urinalysis Methods Microscopy Image Processing, Computer Assisted Health Care Costs Human In Vitro Studies Diagnosis, Laboratory Descriptive Statistics Automation, Laboratory ab: 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. pubtype: Academic Journal doctype: equations & formulas pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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