9786204208077 - reduce overlapping in mammography by deep learning classification: using convolution neural network de kaur, bobbinpreet; sharma, ketan (5 resultados)
REDUCE OVERLAPPING IN MAMMOGRAPHY BY DEEP LEARNING CLASSIFICATION | USING CONVOLUTION NEURAL NETWORK
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Librería: preigu, Osnabrück, Alemaniapreigu
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Taschenbuch. Condición: 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, 490…78 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu.
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Librería: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, AlemaniaBuchWeltWeit Ludwig Meier e.K.
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Taschenbuch. Condición: 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 mort…ality 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.
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Librería: moluna, Greven, Alemaniamoluna
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Condición: 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 amo…ng women. Screening mammogr.
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Librería: buchversandmimpf2000, Emtmannsberg, BAYE, Alemaniabuchversandmimpf2000
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Taschenbuch. Condición: 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 mortalit…y 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.
REDUCE OVERLAPPING IN MAMMOGRAPHY BY DEEP LEARNING CLASSIFICATION : USING CONVOLUTION NEURAL NETWORK
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Librería: AHA-BUCH GmbH, Einbeck, AlemaniaAHA-BUCH GmbH
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EUR 40,89
Envío por EUR 60,63Se envía de Alemania a Estados Unidos de AmericaCantidad disponible: 1 disponibles
Taschenbuch. Condición: 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.
