Deep Learning Solution for Quantification of Fluorescence Particles on a Membrane Article Swipe
Abdellah Zakaria Sellam
,
Azeddine Benlamoudi
,
Clément Cid
,
Léopold Dobelle
,
Amina Slama
,
Yassin El Hillali
,
Abdelmalik Taleb‐Ahmed
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3390/s23041794
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3390/s23041794
The detection and quantification of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) virus particles in ambient waters using a membrane-based in-gel loop-mediated isothermal amplification (mgLAMP) method can play an important role in large-scale environmental surveillance for early warning of potential outbreaks. However, counting particles or cells in fluorescence microscopy is an expensive, time-consuming, and tedious task that only highly trained technicians and researchers can perform. Although such objects are generally easy to identify, manually annotating cells is occasionally prone to fatigue errors and arbitrariness due to the operator’s interpretation of borderline cases. In this research, we proposed a method to detect and quantify multiscale and shape variant SARS-CoV-2 fluorescent cells generated using a portable (mgLAMP) system and captured using a smartphone camera. The proposed method is based on the YOLOv5 algorithm, which uses CSPnet as its backbone. CSPnet is a recently proposed convolutional neural network (CNN) that duplicates gradient information within the network using a combination of Dense nets and ResNet blocks, and bottleneck convolution layers to reduce computation while at the same time maintaining high accuracy. In addition, we apply the test time augmentation (TTA) algorithm in conjunction with YOLO’s one-stage multihead detection heads to detect all cells of varying sizes and shapes. We evaluated the model using a private dataset provided by the Linde + Robinson Laboratory, California Institute of Technology, United States. The model achieved a [email protected] score of 90.3 in the YOLOv5-s6.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/s23041794
- https://www.mdpi.com/1424-8220/23/4/1794/pdf?version=1676540621
- OA Status
- gold
- Cited By
- 3
- References
- 37
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4319319909
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4319319909Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/s23041794Digital Object Identifier
- Title
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Deep Learning Solution for Quantification of Fluorescence Particles on a MembraneWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2023Year of publication
- Publication date
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2023-02-05Full publication date if available
- Authors
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Abdellah Zakaria Sellam, Azeddine Benlamoudi, Clément Cid, Léopold Dobelle, Amina Slama, Yassin El Hillali, Abdelmalik Taleb‐AhmedList of authors in order
- Landing page
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https://doi.org/10.3390/s23041794Publisher landing page
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https://www.mdpi.com/1424-8220/23/4/1794/pdf?version=1676540621Direct link to full text PDF
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://www.mdpi.com/1424-8220/23/4/1794/pdf?version=1676540621Direct OA link when available
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Fluorescence, Membrane, Computer science, Nanotechnology, Chemistry, Artificial intelligence, Chromatography, Materials science, Physics, Optics, BiochemistryTop concepts (fields/topics) attached by OpenAlex
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3Total citation count in OpenAlex
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2025: 1, 2024: 2Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.YOLO’s | 189 |
| abstract_inverted_index.achieved | 226 |
| abstract_inverted_index.captured | 116 |
| abstract_inverted_index.counting | 41 |
| abstract_inverted_index.gradient | 147 |
| abstract_inverted_index.manually | 72 |
| abstract_inverted_index.perform. | 63 |
| abstract_inverted_index.portable | 112 |
| abstract_inverted_index.proposed | 95, 122, 140 |
| abstract_inverted_index.provided | 211 |
| abstract_inverted_index.quantify | 101 |
| abstract_inverted_index.recently | 139 |
| abstract_inverted_index.syndrome | 8 |
| abstract_inverted_index.Institute | 219 |
| abstract_inverted_index.accuracy. | 175 |
| abstract_inverted_index.addition, | 177 |
| abstract_inverted_index.algorithm | 185 |
| abstract_inverted_index.backbone. | 135 |
| abstract_inverted_index.detection | 1, 192 |
| abstract_inverted_index.evaluated | 204 |
| abstract_inverted_index.generally | 68 |
| abstract_inverted_index.generated | 109 |
| abstract_inverted_index.identify, | 71 |
| abstract_inverted_index.important | 28 |
| abstract_inverted_index.multihead | 191 |
| abstract_inverted_index.one-stage | 190 |
| abstract_inverted_index.particles | 12, 42 |
| abstract_inverted_index.potential | 38 |
| abstract_inverted_index.research, | 93 |
| abstract_inverted_index.California | 218 |
| abstract_inverted_index.SARS-CoV-2 | 106 |
| abstract_inverted_index.YOLOv5-s6. | 234 |
| abstract_inverted_index.algorithm, | 129 |
| abstract_inverted_index.annotating | 73 |
| abstract_inverted_index.borderline | 89 |
| abstract_inverted_index.bottleneck | 162 |
| abstract_inverted_index.duplicates | 146 |
| abstract_inverted_index.expensive, | 50 |
| abstract_inverted_index.isothermal | 21 |
| abstract_inverted_index.microscopy | 47 |
| abstract_inverted_index.multiscale | 102 |
| abstract_inverted_index.outbreaks. | 39 |
| abstract_inverted_index.smartphone | 119 |
| abstract_inverted_index.Laboratory, | 217 |
| abstract_inverted_index.Technology, | 221 |
| abstract_inverted_index.combination | 154 |
| abstract_inverted_index.computation | 167 |
| abstract_inverted_index.conjunction | 187 |
| abstract_inverted_index.convolution | 163 |
| abstract_inverted_index.fluorescent | 107 |
| abstract_inverted_index.information | 148 |
| abstract_inverted_index.large-scale | 31 |
| abstract_inverted_index.maintaining | 173 |
| abstract_inverted_index.researchers | 61 |
| abstract_inverted_index.respiratory | 7 |
| abstract_inverted_index.technicians | 59 |
| abstract_inverted_index.(SARS-CoV-2) | 10 |
| abstract_inverted_index.augmentation | 183 |
| abstract_inverted_index.fluorescence | 46 |
| abstract_inverted_index.occasionally | 76 |
| abstract_inverted_index.operator’s | 86 |
| abstract_inverted_index.surveillance | 33 |
| abstract_inverted_index.amplification | 22 |
| abstract_inverted_index.arbitrariness | 82 |
| abstract_inverted_index.convolutional | 141 |
| abstract_inverted_index.coronavirus-2 | 9 |
| abstract_inverted_index.environmental | 32 |
| abstract_inverted_index.loop-mediated | 20 |
| abstract_inverted_index.interpretation | 87 |
| abstract_inverted_index.membrane-based | 18 |
| abstract_inverted_index.quantification | 3 |
| abstract_inverted_index.time-consuming, | 51 |
| cited_by_percentile_year.max | 96 |
| cited_by_percentile_year.min | 91 |
| corresponding_author_ids | https://openalex.org/A5088214859, https://openalex.org/A5033921979 |
| countries_distinct_count | 4 |
| institutions_distinct_count | 7 |
| corresponding_institution_ids | https://openalex.org/I1294671590, https://openalex.org/I2279609970, https://openalex.org/I4210123471, https://openalex.org/I4210155236, https://openalex.org/I70348806 |
| citation_normalized_percentile.value | 0.54427327 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |