{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,20]],"date-time":"2026-01-20T13:21:20Z","timestamp":1768915280722,"version":"3.49.0"},"reference-count":44,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2023,2,5]],"date-time":"2023-02-05T00:00:00Z","timestamp":1675555200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000865","name":"Bill &amp; Melinda Gates Foundation","doi-asserted-by":"publisher","award":["INV-030223"],"award-info":[{"award-number":["INV-030223"]}],"id":[{"id":"10.13039\/100000865","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>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\u2019s 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\u2019s 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.<\/jats:p>","DOI":"10.3390\/s23041794","type":"journal-article","created":{"date-parts":[[2023,2,6]],"date-time":"2023-02-06T02:06:43Z","timestamp":1675649203000},"page":"1794","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Deep Learning Solution for Quantification of Fluorescence Particles on a Membrane"],"prefix":"10.3390","volume":"23","author":[{"given":"Abdellah Zakaria","family":"Sellam","sequence":"first","affiliation":[{"name":"Institute of Applied Sciences and Intelligent Systems, National Research Council of Italy, 73100 Lecce, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2023-2154","authenticated-orcid":false,"given":"Azeddine","family":"Benlamoudi","sequence":"additional","affiliation":[{"name":"Laboratoire de G\u00e9nie \u00c9lectrique, Facult\u00e9 des Nouvelles Technologies de l\u2019Information et de la Communication, Universit\u00e9 Ouargla, Ouargla 30000, Algeria"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7293-035X","authenticated-orcid":false,"given":"Cl\u00e9ment Antoine","family":"Cid","sequence":"additional","affiliation":[{"name":"Linde Laboratories, California Institute of Technology, Pasadena, CA 91125, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Leopold","family":"Dobelle","sequence":"additional","affiliation":[{"name":"Linde Laboratories, California Institute of Technology, Pasadena, CA 91125, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Amina","family":"Slama","sequence":"additional","affiliation":[{"name":"Faculty of Humanities and Social Sciences, Mohamed Khider University of Biskra, Biskra 07000, Algeria"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3980-9902","authenticated-orcid":false,"given":"Yassin","family":"El Hillali","sequence":"additional","affiliation":[{"name":"Institut d\u2019Electronique de Micro\u00e9lectronique et de Nanotechnologie (IEMN), UMR 8520, Universit\u00e9 Polytechnique Hauts de France, Universit\u00e9 de Lille, CNRS, 59313 Valenciennes, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7218-3799","authenticated-orcid":false,"given":"Abdelmalik","family":"Taleb-Ahmed","sequence":"additional","affiliation":[{"name":"Institut d\u2019Electronique de Micro\u00e9lectronique et de Nanotechnologie (IEMN), UMR 8520, Universit\u00e9 Polytechnique Hauts de France, Universit\u00e9 de Lille, CNRS, 59313 Valenciennes, France"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,2,5]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"408","DOI":"10.1038\/s41586-021-04188-6","article-title":"The emergence, genomic diversity and global spread of SARS-CoV-2","volume":"600","author":"Li","year":"2021","journal-title":"Nature"},{"key":"ref_2","unstructured":"Bivins, A., North, D., Ahmad, A., Ahmed, W., Alm, E., Been, F., Bhattacharya, P., Bijlsma, L., Boehm, A.B., and Brown, J. 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