Applications of Digital Microscopy and Densely Connected Convolutional Neural Networks for Automated Quantification of Babesia-Infected Erythrocytes
Durant TJS, Dudgeon SN, McPadden J, Simpson A, Price N, Schulz WL, Torres R, Olson EM. Applications of Digital Microscopy and Densely Connected Convolutional Neural Networks for Automated Quantification of Babesia-Infected Erythrocytes. Clinical Chemistry 2021, 68: 218-229. PMID: 34969114, DOI: 10.1093/clinchem/hvab237.Peer-Reviewed Original ResearchVery Deep Convolutional Neural Networks for Morphologic Classification of Erythrocytes
Durant T, Olson EM, Schulz WL, Torres R. Very Deep Convolutional Neural Networks for Morphologic Classification of Erythrocytes. Clinical Chemistry 2017, 63: 1847-1855. PMID: 28877918, DOI: 10.1373/clinchem.2017.276345.Peer-Reviewed Original ResearchMeSH KeywordsDatabases, FactualErythrocytesHumansImage Processing, Computer-AssistedNeural Networks, ComputerConceptsConvolutional neural networkNeural networkDeep convolutional neural networkDense shortcut connectionsNeural network designSlow manual processSignificant labor costsClassification of erythrocytesWeb applicationCapable machinesUnseen dataShortcut connectionsManual processEnsemble model predictionsDigital imagesPrecision metricsArchitectural considerationsNetwork designAutomated profilingClassification frequencyMisclassification errorPractical performanceFinal databaseHarmonic meanNetwork