Matches in SemOpenAlex for { <https://semopenalex.org/work/W4226063024> ?p ?o ?g. }
- W4226063024 endingPage "1401" @default.
- W4226063024 startingPage "1395" @default.
- W4226063024 abstract "The rapid recognition of fetal nucleated red blood cells (fNRBCs) presents considerable challenges.To establish a computer-aided diagnosis system for rapid recognition of fNRBCs by convolutional neural network.We adopted density gradient centrifugation and magnetic-activated cell sorting to extract fNRBCs from umbilical cord blood samples. The cell-block method was used to embed fNRBCs for routine formalin-fixed paraffin sectioning and hematoxylin-eosin staining. Then, we proposed a convolutional neural network-based, computer-aided diagnosis system to automatically discriminate features and recognize fNRBCs. Extracting methods of interested region were used to automatically segment individual cells in cell slices. The discriminant information from cellular-level regions of interest was encoded into a feature vector. Pathologic diagnoses were also provided by the network.In total, 4760 pictures of fNRBCs from 260 cell-slides of 4 umbilical cord blood samples were collected. On the premise of 100% accuracy in the training set (3720 pictures), the sensitivity, specificity, and accuracy of cellular intelligent recognition were 96.5%, 100%, and 98.5%, respectively, in the test set (1040 pictures).We established a computer-aided diagnosis system for effective and accurate fNRBC recognition based on a convolutional neural network." @default.
- W4226063024 created "2022-05-05" @default.
- W4226063024 creator A5001070240 @default.
- W4226063024 creator A5004139292 @default.
- W4226063024 creator A5007309170 @default.
- W4226063024 creator A5019074796 @default.
- W4226063024 creator A5020071899 @default.
- W4226063024 creator A5040554547 @default.
- W4226063024 creator A5050248746 @default.
- W4226063024 creator A5063325537 @default.
- W4226063024 creator A5071773009 @default.
- W4226063024 creator A5073216396 @default.
- W4226063024 creator A5087363413 @default.
- W4226063024 date "2022-03-16" @default.
- W4226063024 modified "2023-09-26" @default.
- W4226063024 title "A Computer-Aided Diagnosis System of Fetal Nucleated Red Blood Cells With Convolutional Neural Network" @default.
- W4226063024 cites W1514949855 @default.
- W4226063024 cites W1561040411 @default.
- W4226063024 cites W1970280628 @default.
- W4226063024 cites W1993948273 @default.
- W4226063024 cites W2010162458 @default.
- W4226063024 cites W2018718962 @default.
- W4226063024 cites W2022418588 @default.
- W4226063024 cites W2043538618 @default.
- W4226063024 cites W2043651556 @default.
- W4226063024 cites W2045009689 @default.
- W4226063024 cites W2058356552 @default.
- W4226063024 cites W2066796679 @default.
- W4226063024 cites W2072721747 @default.
- W4226063024 cites W2079330268 @default.
- W4226063024 cites W2085959899 @default.
- W4226063024 cites W2100786643 @default.
- W4226063024 cites W2112331560 @default.
- W4226063024 cites W2137164588 @default.
- W4226063024 cites W2137175883 @default.
- W4226063024 cites W2137403073 @default.
- W4226063024 cites W2142498970 @default.
- W4226063024 cites W2152397906 @default.
- W4226063024 cites W2152493043 @default.
- W4226063024 cites W2194775991 @default.
- W4226063024 cites W2519803632 @default.
- W4226063024 cites W2547221468 @default.
- W4226063024 cites W2581082771 @default.
- W4226063024 cites W2592929672 @default.
- W4226063024 cites W2771186014 @default.
- W4226063024 cites W2783710041 @default.
- W4226063024 cites W2793904719 @default.
- W4226063024 cites W2803760365 @default.
- W4226063024 cites W2807098876 @default.
- W4226063024 cites W2886801379 @default.
- W4226063024 cites W2887005225 @default.
- W4226063024 cites W2887554272 @default.
- W4226063024 cites W2887900619 @default.
- W4226063024 cites W2903071242 @default.
- W4226063024 cites W2912494866 @default.
- W4226063024 cites W2913798402 @default.
- W4226063024 cites W2950014010 @default.
- W4226063024 cites W2963880707 @default.
- W4226063024 cites W2966285781 @default.
- W4226063024 cites W2969244637 @default.
- W4226063024 cites W2979940322 @default.
- W4226063024 cites W2982092517 @default.
- W4226063024 cites W2989693433 @default.
- W4226063024 cites W3037585989 @default.
- W4226063024 cites W3099481908 @default.
- W4226063024 cites W4320800843 @default.
- W4226063024 doi "https://doi.org/10.5858/arpa.2021-0142-oa" @default.
- W4226063024 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/35293972" @default.
- W4226063024 hasPublicationYear "2022" @default.
- W4226063024 type Work @default.
- W4226063024 citedByCount "0" @default.
- W4226063024 crossrefType "journal-article" @default.
- W4226063024 hasAuthorship W4226063024A5001070240 @default.
- W4226063024 hasAuthorship W4226063024A5004139292 @default.
- W4226063024 hasAuthorship W4226063024A5007309170 @default.
- W4226063024 hasAuthorship W4226063024A5019074796 @default.
- W4226063024 hasAuthorship W4226063024A5020071899 @default.
- W4226063024 hasAuthorship W4226063024A5040554547 @default.
- W4226063024 hasAuthorship W4226063024A5050248746 @default.
- W4226063024 hasAuthorship W4226063024A5063325537 @default.
- W4226063024 hasAuthorship W4226063024A5071773009 @default.
- W4226063024 hasAuthorship W4226063024A5073216396 @default.
- W4226063024 hasAuthorship W4226063024A5087363413 @default.
- W4226063024 hasBestOaLocation W42260630241 @default.
- W4226063024 hasConcept C125473707 @default.
- W4226063024 hasConcept C138885662 @default.
- W4226063024 hasConcept C142724271 @default.
- W4226063024 hasConcept C153180895 @default.
- W4226063024 hasConcept C154945302 @default.
- W4226063024 hasConcept C169903167 @default.
- W4226063024 hasConcept C203014093 @default.
- W4226063024 hasConcept C2776401178 @default.
- W4226063024 hasConcept C2776955114 @default.
- W4226063024 hasConcept C41008148 @default.
- W4226063024 hasConcept C41895202 @default.
- W4226063024 hasConcept C50644808 @default.
- W4226063024 hasConcept C58489278 @default.
- W4226063024 hasConcept C71924100 @default.