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Deep Learning Solution for Quantification of Fluorescence Particles on a Membrane
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 warni...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9967937/ https://www.ncbi.nlm.nih.gov/pubmed/36850392 http://dx.doi.org/10.3390/s23041794 |
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author | Sellam, Abdellah Zakaria Benlamoudi, Azeddine Cid, Clément Antoine Dobelle, Leopold Slama, Amina El Hillali, Yassin Taleb-Ahmed, Abdelmalik |
author_facet | Sellam, Abdellah Zakaria Benlamoudi, Azeddine Cid, Clément Antoine Dobelle, Leopold Slama, Amina El Hillali, Yassin Taleb-Ahmed, Abdelmalik |
author_sort | Sellam, Abdellah Zakaria |
collection | PubMed |
description | 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 mAP@0.5 score of 90.3 in the YOLOv5-s6. |
format | Online Article Text |
id | pubmed-9967937 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-99679372023-02-27 Deep Learning Solution for Quantification of Fluorescence Particles on a Membrane Sellam, Abdellah Zakaria Benlamoudi, Azeddine Cid, Clément Antoine Dobelle, Leopold Slama, Amina El Hillali, Yassin Taleb-Ahmed, Abdelmalik Sensors (Basel) Article 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 mAP@0.5 score of 90.3 in the YOLOv5-s6. MDPI 2023-02-05 /pmc/articles/PMC9967937/ /pubmed/36850392 http://dx.doi.org/10.3390/s23041794 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Sellam, Abdellah Zakaria Benlamoudi, Azeddine Cid, Clément Antoine Dobelle, Leopold Slama, Amina El Hillali, Yassin Taleb-Ahmed, Abdelmalik Deep Learning Solution for Quantification of Fluorescence Particles on a Membrane |
title | Deep Learning Solution for Quantification of Fluorescence Particles on a Membrane |
title_full | Deep Learning Solution for Quantification of Fluorescence Particles on a Membrane |
title_fullStr | Deep Learning Solution for Quantification of Fluorescence Particles on a Membrane |
title_full_unstemmed | Deep Learning Solution for Quantification of Fluorescence Particles on a Membrane |
title_short | Deep Learning Solution for Quantification of Fluorescence Particles on a Membrane |
title_sort | deep learning solution for quantification of fluorescence particles on a membrane |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9967937/ https://www.ncbi.nlm.nih.gov/pubmed/36850392 http://dx.doi.org/10.3390/s23041794 |
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