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Automated Detection of Hematological Patterns Through Machine Learning: Using Feature Extraction And Artificial Neural Networks for Pattern Recognition - Brossura
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The use of hematological analyzers has become routine in clinical practice, but the sheer volume of data produced by these devices often makes manual inspection of all the results an unwieldy task. For this reason, automated pattern analysis through the use of machine learning has been used in these types of situations, to save time and to provide invaluable aid to medical professionals in this area of diagnostic medicine. Toward this end, Artificial Neural Networks (ANNs) are often relied upon in the field of machine learning, because of their ability to distill representative feature components from large amounts of input data. This paper details an approach in which the scatterplots of cells that were produced by a hematological device were used as inputs. The data were separated into two classes, one containing clinically Normal samples, and the second containing abnormal samples that contained Variant Lymphocytes. Statistical features were extracted from these data using Principal Component Analysis (PCA) and then a Perceptron ANN was employed to differentiate between the two classes of data. The accuracy of pattern classification using this method was then discussed.
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B.S. in Electrical Engineering, Florida International University, Miami, Florida, 1999.M.S in Computer Engineering, Florida International University, Miami, Florida, 2003.Ph.D. in Electrical Engineering, Florida International University, Miami, Florida, 2011.Senior Software Engineer, Beckman Coulter Corporation, Miami, Florida, 2004-Present.
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- EditoreLAP LAMBERT Academic Publishing
- Data di pubblicazione2014
- ISBN 10 3659333654
- ISBN 13 9783659333651
- RilegaturaCopertina flessibile
- LinguaInglese
- Numero di pagine128
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Automated Detection of Hematological Patterns Through Machine Learning
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LAP LAMBERT Academic Publishing Jul 2014, 2014
ISBN 10: 3659333654
ISBN 13: 9783659333651
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Da: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germania
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Taschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -The use of hematological analyzers has become routine in clinical practice, but the sheer volume of data produced by these devices often makes manual inspection of all the results an unwieldy task. For this reason, automated pattern analysis through the use of machine learning has been used in these types of situations, to save time and to provide invaluable aid to medical professionals in this area of diagnostic medicine. Toward this end, Artificial Neural Networks (ANNs) are often relied upon in the field of machine learning, because of their ability to distill representative feature components from large amounts of input data. This paper details an approach in which the scatterplots of cells that were produced by a hematological device were used as inputs. The data were separated into two classes, one containing clinically Normal samples, and the second containing abnormal samples that contained Variant Lymphocytes. Statistical features were extracted from these data using Principal Component Analysis (PCA) and then a Perceptron ANN was employed to differentiate between the two classes of data. The accuracy of pattern classification using this method was then discussed. 128 pp. Englisch. Codice articolo 9783659333651
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Automated Detection of Hematological Patterns Through Machine Learning
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VDM Verlag Dr. Mueller Aktiengesellschaft & Co. KG, 2014
ISBN 10: 3659333654
ISBN 13: 9783659333651
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Da: Books Puddle, New York, NY, U.S.A.
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Condizione: New. pp. 128. Codice articolo 26128412829
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Automated Detection of Hematological Patterns Through Machine Learning
Editore:
VDM Verlag Dr. Mueller Aktiengesellschaft & Co. KG, 2014
ISBN 10: 3659333654
ISBN 13: 9783659333651
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Da: Majestic Books, Hounslow, Regno Unito
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Condizione: New. Print on Demand pp. 128 2:B&W 6 x 9 in or 229 x 152 mm Perfect Bound on Creme w/Gloss Lam. Codice articolo 131126082
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Automated Detection of Hematological Patterns Through Machine Learning : Using Feature Extraction And Artificial Neural Networks for Pattern Recognition
Editore:
LAP LAMBERT Academic Publishing, 2014
ISBN 10: 3659333654
ISBN 13: 9783659333651
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Da: AHA-BUCH GmbH, Einbeck, Germania
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Taschenbuch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - The use of hematological analyzers has become routine in clinical practice, but the sheer volume of data produced by these devices often makes manual inspection of all the results an unwieldy task. For this reason, automated pattern analysis through the use of machine learning has been used in these types of situations, to save time and to provide invaluable aid to medical professionals in this area of diagnostic medicine. Toward this end, Artificial Neural Networks (ANNs) are often relied upon in the field of machine learning, because of their ability to distill representative feature components from large amounts of input data. This paper details an approach in which the scatterplots of cells that were produced by a hematological device were used as inputs. The data were separated into two classes, one containing clinically Normal samples, and the second containing abnormal samples that contained Variant Lymphocytes. Statistical features were extracted from these data using Principal Component Analysis (PCA) and then a Perceptron ANN was employed to differentiate between the two classes of data. The accuracy of pattern classification using this method was then discussed. Codice articolo 9783659333651
Quantità: 1 disponibili
Foto dell'editore
Automated Detection of Hematological Patterns Through Machine Learning
Editore:
VDM Verlag Dr. Mueller Aktiengesellschaft & Co. KG, 2014
ISBN 10: 3659333654
ISBN 13: 9783659333651
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Da: Biblios, Frankfurt am main, HESSE, Germania
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Condizione: New. PRINT ON DEMAND pp. 128. Codice articolo 18128412823
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Automated Detection of Hematological Patterns Through Machine Learning
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LAP LAMBERT Academic Publishing, 2014
ISBN 10: 3659333654
ISBN 13: 9783659333651
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Da: moluna, Greven, Germania
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Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Rossman MarkB.S. in Electrical Engineering, Florida International University, Miami, Florida, 1999.M.S in Computer Engineering, Florida International University, Miami, Florida, 2003.Ph.D. in Electrical Engineering, Florida Internati. Codice articolo 5149234
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