Search alternatives:
precision classification » lesion classification (Expand Search), emotion classification (Expand Search), protein classification (Expand Search)
codon optimization » wolf optimization (Expand Search)
care precision » score precision (Expand Search), curve precision (Expand Search), care provision (Expand Search)
binary care » primary care (Expand Search), binary image (Expand Search), binary pairs (Expand Search)
binary 2 » binary _ (Expand Search), binary b (Expand Search)
2 codon » _ codon (Expand Search)
precision classification » lesion classification (Expand Search), emotion classification (Expand Search), protein classification (Expand Search)
codon optimization » wolf optimization (Expand Search)
care precision » score precision (Expand Search), curve precision (Expand Search), care provision (Expand Search)
binary care » primary care (Expand Search), binary image (Expand Search), binary pairs (Expand Search)
binary 2 » binary _ (Expand Search), binary b (Expand Search)
2 codon » _ codon (Expand Search)
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Deactivated Cas9-Engineered Magnetic Micromotors toward a Point-of-Care Digital Viral RNA Assay
Published 2025“…A convolutional neural network classification-based multiobject tracking algorithm, CNN-MOT, accurately measures the change in micromotor motion, facilitating the binary digital assay format (“1” or “0”) for simplified result interpretation without user bias. …”
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Deactivated Cas9-Engineered Magnetic Micromotors toward a Point-of-Care Digital Viral RNA Assay
Published 2025“…A convolutional neural network classification-based multiobject tracking algorithm, CNN-MOT, accurately measures the change in micromotor motion, facilitating the binary digital assay format (“1” or “0”) for simplified result interpretation without user bias. …”
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4
Deactivated Cas9-Engineered Magnetic Micromotors toward a Point-of-Care Digital Viral RNA Assay
Published 2025“…A convolutional neural network classification-based multiobject tracking algorithm, CNN-MOT, accurately measures the change in micromotor motion, facilitating the binary digital assay format (“1” or “0”) for simplified result interpretation without user bias. …”
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5
Deactivated Cas9-Engineered Magnetic Micromotors toward a Point-of-Care Digital Viral RNA Assay
Published 2025“…A convolutional neural network classification-based multiobject tracking algorithm, CNN-MOT, accurately measures the change in micromotor motion, facilitating the binary digital assay format (“1” or “0”) for simplified result interpretation without user bias. …”
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6
Deactivated Cas9-Engineered Magnetic Micromotors toward a Point-of-Care Digital Viral RNA Assay
Published 2025“…A convolutional neural network classification-based multiobject tracking algorithm, CNN-MOT, accurately measures the change in micromotor motion, facilitating the binary digital assay format (“1” or “0”) for simplified result interpretation without user bias. …”
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7
Deactivated Cas9-Engineered Magnetic Micromotors toward a Point-of-Care Digital Viral RNA Assay
Published 2025“…A convolutional neural network classification-based multiobject tracking algorithm, CNN-MOT, accurately measures the change in micromotor motion, facilitating the binary digital assay format (“1” or “0”) for simplified result interpretation without user bias. …”
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8
Deactivated Cas9-Engineered Magnetic Micromotors toward a Point-of-Care Digital Viral RNA Assay
Published 2025“…A convolutional neural network classification-based multiobject tracking algorithm, CNN-MOT, accurately measures the change in micromotor motion, facilitating the binary digital assay format (“1” or “0”) for simplified result interpretation without user bias. …”
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9
Image3_DFUCare: deep learning platform for diabetic foot ulcer detection, analysis, and monitoring.jpeg
Published 2024“…</p>Results<p>DFUCare achieved an F1-score of 0.80 and a mean Average Precision (mAP) of 0.861 for wound localization. For infection classification, we obtained a binary accuracy of 79.76%, while ischemic classification reached 94.81% on the validation set. …”
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10
Image4_DFUCare: deep learning platform for diabetic foot ulcer detection, analysis, and monitoring.jpeg
Published 2024“…</p>Results<p>DFUCare achieved an F1-score of 0.80 and a mean Average Precision (mAP) of 0.861 for wound localization. For infection classification, we obtained a binary accuracy of 79.76%, while ischemic classification reached 94.81% on the validation set. …”
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11
Image1_DFUCare: deep learning platform for diabetic foot ulcer detection, analysis, and monitoring.jpeg
Published 2024“…</p>Results<p>DFUCare achieved an F1-score of 0.80 and a mean Average Precision (mAP) of 0.861 for wound localization. For infection classification, we obtained a binary accuracy of 79.76%, while ischemic classification reached 94.81% on the validation set. …”
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12
Table1_DFUCare: deep learning platform for diabetic foot ulcer detection, analysis, and monitoring.docx
Published 2024“…</p>Results<p>DFUCare achieved an F1-score of 0.80 and a mean Average Precision (mAP) of 0.861 for wound localization. For infection classification, we obtained a binary accuracy of 79.76%, while ischemic classification reached 94.81% on the validation set. …”
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13
Image2_DFUCare: deep learning platform for diabetic foot ulcer detection, analysis, and monitoring.jpeg
Published 2024“…</p>Results<p>DFUCare achieved an F1-score of 0.80 and a mean Average Precision (mAP) of 0.861 for wound localization. For infection classification, we obtained a binary accuracy of 79.76%, while ischemic classification reached 94.81% on the validation set. …”
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14
Image5_DFUCare: deep learning platform for diabetic foot ulcer detection, analysis, and monitoring.jpeg
Published 2024“…</p>Results<p>DFUCare achieved an F1-score of 0.80 and a mean Average Precision (mAP) of 0.861 for wound localization. For infection classification, we obtained a binary accuracy of 79.76%, while ischemic classification reached 94.81% on the validation set. …”