{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,3]],"date-time":"2026-06-03T22:57:21Z","timestamp":1780527441795,"version":"3.54.1"},"reference-count":49,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2020,11,7]],"date-time":"2020-11-07T00:00:00Z","timestamp":1604707200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003407","name":"Ministero dell\u2019Istruzione, dell\u2019Universit\u00e0 e della Ricerca","doi-asserted-by":"publisher","award":["PON ARS01_01183"],"award-info":[{"award-number":["PON ARS01_01183"]}],"id":[{"id":"10.13039\/501100003407","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Diatoms are among the dominant phytoplankters in marine and freshwater habitats, and important biomarkers of water quality, making their identification and classification one of the current challenges for environmental monitoring. To date, taxonomy of the species populating a water column is still conducted by marine biologists on the basis of their own experience. On the other hand, deep learning is recognized as the elective technique for solving image classification problems. However, a large amount of training data is usually needed, thus requiring the synthetic enlargement of the dataset through data augmentation. In the case of microalgae, the large variety of species that populate the marine environments makes it arduous to perform an exhaustive training that considers all the possible classes. However, commercial test slides containing one diatom element per class fixed in between two glasses are available on the market. These are usually prepared by expert diatomists for taxonomy purposes, thus constituting libraries of the populations that can be found in oceans. Here we show that such test slides are very useful for training accurate deep Convolutional Neural Networks (CNNs). We demonstrate the successful classification of diatoms based on a proper CNNs ensemble and a fully augmented dataset, i.e., creation starting from one single image per class available from a commercial glass slide containing 50 fixed species in a dry setting. This approach avoids the time-consuming steps of water sampling and labeling by skilled marine biologists. To accomplish this goal, we exploit the holographic imaging modality, which permits the accessing of a quantitative phase-contrast maps and a posteriori flexible refocusing due to its intrinsic 3D imaging capability. The network model is then validated by using holographic recordings of live diatoms imaged in water samples i.e., in their natural wet environmental condition.<\/jats:p>","DOI":"10.3390\/s20216353","type":"journal-article","created":{"date-parts":[[2020,11,8]],"date-time":"2020-11-08T19:03:37Z","timestamp":1604862217000},"page":"6353","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":34,"title":["Learning Diatoms Classification from a Dry Test Slide by Holographic Microscopy"],"prefix":"10.3390","volume":"20","author":[{"given":"Pasquale","family":"Memmolo","sequence":"first","affiliation":[{"name":"Institute of Applied Sciences and Intelligent Systems (ISASI) National Research Council (CNR) of Italy, Via Campi Flegrei 34, 80078 Pozzuoli, NA, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pierluigi","family":"Carcagn\u00ec","sequence":"additional","affiliation":[{"name":"Institute of Applied Sciences and Intelligent Systems (ISASI) National Research Council (CNR) of Italy, Via Monteorni snc University Campus, 73100 Lecce, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1956-4976","authenticated-orcid":false,"given":"Vittorio","family":"Bianco","sequence":"additional","affiliation":[{"name":"Institute of Applied Sciences and Intelligent Systems (ISASI) National Research Council (CNR) of Italy, Via Campi Flegrei 34, 80078 Pozzuoli, NA, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Francesco","family":"Merola","sequence":"additional","affiliation":[{"name":"Institute of Applied Sciences and Intelligent Systems (ISASI) National Research Council (CNR) of Italy, Via Campi Flegrei 34, 80078 Pozzuoli, NA, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0579-8464","authenticated-orcid":false,"given":"Andouglas","family":"Goncalves da Silva Junior","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering and Automation, Federal University of Rio Grande do Norte, 59078 Natal, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7735-5630","authenticated-orcid":false,"given":"Luis Marcos","family":"Garcia Goncalves","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering and Automation, Federal University of Rio Grande do Norte, 59078 Natal, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pietro","family":"Ferraro","sequence":"additional","affiliation":[{"name":"Institute of Applied Sciences and Intelligent Systems (ISASI) National Research Council (CNR) of Italy, Via Campi Flegrei 34, 80078 Pozzuoli, NA, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1073-2390","authenticated-orcid":false,"given":"Cosimo","family":"Distante","sequence":"additional","affiliation":[{"name":"Institute of Applied Sciences and Intelligent Systems (ISASI) National Research Council (CNR) of Italy, Via Monteorni snc University Campus, 73100 Lecce, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,11,7]]},"reference":[{"key":"ref_1","first-page":"39","article-title":"Water quality assessment\u2013a review","volume":"4","author":"Shyamala","year":"2017","journal-title":"Int. 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