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Nevertheless, the vast number of images generated by imaging flow cytometry imposes great challenges for data analysis especially when the species have similar morphologies. In this work, we report a deep learning\u2010enabled high\u2010throughput system for predicting <jats:italic>Cryptosporidium<\/jats:italic> and <jats:italic>Giardia<\/jats:italic> in drinking water. This system combines imaging flow cytometry and an efficient artificial neural network called MCellNet, which achieves a classification accuracy &gt;99.6%. The system can detect <jats:italic>Cryptosporidium<\/jats:italic> and <jats:italic>Giardia<\/jats:italic> with a sensitivity of 97.37% and a specificity of 99.95%. The high\u2010speed analysis reaches 346 frames per second, outperforming the state\u2010of\u2010the\u2010art deep learning algorithm MobileNetV2 in speed (251 frames per second) with a comparable classification accuracy. The reported system empowers rapid, accurate, and high throughput bioparticle detection in clinical diagnostics, environmental monitoring and other potential biosensing applications.<\/jats:p>","DOI":"10.1002\/cyto.a.24321","type":"journal-article","created":{"date-parts":[[2021,2,8]],"date-time":"2021-02-08T19:08:09Z","timestamp":1612811289000},"page":"1123-1133","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":34,"title":["Deep <scp>learning\u2010enabled<\/scp> imaging flow cytometry for <scp>high\u2010speed<\/scp><i>Cryptosporidium<\/i> and <i>Giardia<\/i> detection"],"prefix":"10.1002","volume":"99","author":[{"given":"Shaobo","family":"Luo","sequence":"first","affiliation":[{"name":"ESIEE Universite Paris\u2010Est  Noisy\u2010le\u2010Grand Cedex France"},{"name":"Nanyang Environment and Water Research Institute Nanyang Technological University  Singapore Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kim Truc","family":"Nguyen","sequence":"additional","affiliation":[{"name":"Nanyang Environment and Water Research Institute Nanyang Technological University  Singapore Singapore"},{"name":"School of Electrical &amp; Electronic Engineering Nanyang Technological University  Singapore Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Binh T. 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