REDUCE OVERLAPPING IN MAMMOGRAPHY BY DEEP LEARNING CLASSIFICATION: USING CONVOLUTION NEURAL NETWORK - Couverture souple
Synopsis
Breast cancer is the leading cause of cancer death among women. Screening mammography is the only method currently available for the reliable detection of early and potentially curable breast cancer. Research indicates that the mortality rate could decrease by 30% if women age 50 and older have regular mammograms. In this dissertation, we propose a new full-field mammogram analysis method focusing on characterizing and identifying normal mammograms. A mammogram is analyzed region by region and is classified as normal or abnormal. The methods for extracting features are presented in this thesis which are used to distinguish normal and abnormal regions of a mammogram. In this book, convolution neural network classifier is used to boost the classification performance. This classifier performs better than previous classifiers. In that it shows more accuracy than the others classifiers, the misclassification rate of normal mammograms as abnormal.This approach performs good on overlapping problem.
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REDUCE OVERLAPPING IN MAMMOGRAPHY BY DEEP LEARNING CLASSIFICATION
Edité par
LAP LAMBERT Academic Publishing Sep 2021, 2021
ISBN 10 : 6204208071
ISBN 13 : 9786204208077
Neuf
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Vendeur : BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Allemagne
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Taschenbuch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Breast cancer is the leading cause of cancer death among women. Screening mammography is the only method currently available for the reliable detection of early and potentially curable breast cancer. Research indicates that the mortality rate could decrease by 30% if women age 50 and older have regular mammograms. In this dissertation, we propose a new full-field mammogram analysis method focusing on characterizing and identifying normal mammograms. A mammogram is analyzed region by region and is classified as normal or abnormal. The methods for extracting features are presented in this thesis which are used to distinguish normal and abnormal regions of a mammogram. In this book, convolution neural network classifier is used to boost the classification performance. This classifier performs better than previous classifiers. In that it shows more accuracy than the others classifiers, the misclassification rate of normal mammograms as abnormal.This approach performs good on overlapping problem. 72 pp. Englisch. N° de réf. du vendeur 9786204208077
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REDUCE OVERLAPPING IN MAMMOGRAPHY BY DEEP LEARNING CLASSIFICATION
Edité par
LAP LAMBERT Academic Publishing, 2021
ISBN 10 : 6204208071
ISBN 13 : 9786204208077
Neuf
Couverture souple
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Etat : New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Kaur BobbinpreetWorking in the field of Image Processing, my major research area includes disease detection through various machine learning models.Breast cancer is the leading cause of cancer death among women. Screening mammogr. N° de réf. du vendeur 522554936
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REDUCE OVERLAPPING IN MAMMOGRAPHY BY DEEP LEARNING CLASSIFICATION
Edité par
LAP LAMBERT Academic Publishing Sep 2021, 2021
ISBN 10 : 6204208071
ISBN 13 : 9786204208077
Neuf
Taschenbuch
Vendeur : buchversandmimpf2000, Emtmannsberg, BAYE, Allemagne
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Taschenbuch. Etat : Neu. This item is printed on demand - Print on Demand Titel. Neuware -Breast cancer is the leading cause of cancer death among women. Screening mammography is the only method currently available for the reliable detection of early and potentially curable breast cancer. Research indicates that the mortality rate could decrease by 30% if women age 50 and older have regular mammograms. In this dissertation, we propose a new full-field mammogram analysis method focusing on characterizing and identifying normal mammograms. A mammogram is analyzed region by region and is classified as normal or abnormal. The methods for extracting features are presented in this thesis which are used to distinguish normal and abnormal regions of a mammogram. In this book, convolution neural network classifier is used to boost the classification performance. This classifier performs better than previous classifiers. In that it shows more accuracy than the others classifiers, the misclassification rate of normal mammograms as abnormal.This approach performs good on overlapping problem.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 72 pp. Englisch. N° de réf. du vendeur 9786204208077
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REDUCE OVERLAPPING IN MAMMOGRAPHY BY DEEP LEARNING CLASSIFICATION : USING CONVOLUTION NEURAL NETWORK
Edité par
LAP LAMBERT Academic Publishing, 2021
ISBN 10 : 6204208071
ISBN 13 : 9786204208077
Neuf
Taschenbuch
Vendeur : AHA-BUCH GmbH, Einbeck, Allemagne
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Taschenbuch. Etat : Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Breast cancer is the leading cause of cancer death among women. Screening mammography is the only method currently available for the reliable detection of early and potentially curable breast cancer. Research indicates that the mortality rate could decrease by 30% if women age 50 and older have regular mammograms. In this dissertation, we propose a new full-field mammogram analysis method focusing on characterizing and identifying normal mammograms. A mammogram is analyzed region by region and is classified as normal or abnormal. The methods for extracting features are presented in this thesis which are used to distinguish normal and abnormal regions of a mammogram. In this book, convolution neural network classifier is used to boost the classification performance. This classifier performs better than previous classifiers. In that it shows more accuracy than the others classifiers, the misclassification rate of normal mammograms as abnormal.This approach performs good on overlapping problem. N° de réf. du vendeur 9786204208077
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REDUCE OVERLAPPING IN MAMMOGRAPHY BY DEEP LEARNING CLASSIFICATION | USING CONVOLUTION NEURAL NETWORK
Edité par
LAP LAMBERT Academic Publishing, 2021
ISBN 10 : 6204208071
ISBN 13 : 9786204208077
Neuf
Taschenbuch
Vendeur : preigu, Osnabrück, Allemagne
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Taschenbuch. Etat : Neu. REDUCE OVERLAPPING IN MAMMOGRAPHY BY DEEP LEARNING CLASSIFICATION | USING CONVOLUTION NEURAL NETWORK | Bobbinpreet Kaur (u. a.) | Taschenbuch | Englisch | 2021 | LAP LAMBERT Academic Publishing | EAN 9786204208077 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu. N° de réf. du vendeur 120753435
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