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Multi-Class Urinary Sediment Particles Detection Based on ...
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由 T Komori 著作2024 — This paper YOLOv7-based urinary sediment detection with attention modules. YOLOv7 is one of state-of-the-art models, and we additionally implement attention ...
Multi-Class Urinary Sediment Particles Detection Based on ...
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由 T Komori 著作2024 — Section III proposes our designed urinary sediment particle detector, which is based on YOLOv7 with attention modules. Section IV compares our ...
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Automated detection of multi-class urinary sediment particles
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The proposed model is not only based on YOLOX but also takes advantage of data augmentation, and novel loss function, which is called the varifocal loss to ...
Confusion matrix of the YOLOv7-CBAM
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FIGURE 13: Detection of Epith particles more than labels: (Left) Each. Multi-Class Urinary Sediment Particles Detection Based on YOLOv7 With Attention Modules.
Urine Sediment Analysis by Using Convolution Neural ...
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Multi-Class Urinary Sediment Particles Detection Based on YOLOv7 With Attention Modules · Computer Science, Medicine. IEEE Access · 2024.
Research on Urine Sediment Images Recognition Based ...
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Multi-Class Urinary Sediment Particles Detection Based on YOLOv7 With Attention Modules · Computer Science, Medicine. IEEE Access · 2024.
Hiroki Nishikawa
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2024年12月13日 — Multi-Class Urinary Sediment Particles Detection Based on YOLOv7 With Attention Modules. IEEE Access 12: 129753-129764 (2024). [j14]. view.
Automated Urine Microscopy Using Scale Invariant Feature ...
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由 JCD Cruz 著作2019被引用 3 次 — (2024)Multi-Class Urinary Sediment Particles Detection Based on YOLOv7 With Attention ModulesIEEE Access10.1109/ACCESS.2024.344826212(129753-129764)Online ...
Automated detection of multi-class urinary sediment particles ...
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由 H Lyu 著作2023被引用 4 次 — This research aims to mitigate the adverse effects of urine sediment particles while improving multi-class detection performance. We proposed an innovative ...
Highly Differentiated Target Detection under Extremely ...
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由 H Shao 著作2024被引用 1 次 — This paper expounds upon a novel target detection methodology distinguished by its elevated discriminatory efficacy, specifically tailored for environments ...