Machine Learning in Bio-Signal Analysis and Diagnostic Imaging
Machine Learning in Bio-Signal Analysis and Diagnostic Imaging presents original research on the advanced analysis and classification techniques of biomedical signals and images that cover both supervised and unsupervised machine learning models, standards, algorithms, and their applications, along...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Ashour, Amira - 1975- [herausgeberIn] Shi, Fuqian [herausgeberIn] Dey, Nilanjan - 1984- [herausgeberIn] Borra, Surekha [herausgeberIn] |
|---|
| Format: |
E-Book |
|---|---|
| Sprache: |
Englisch |
| Erschienen: |
London: Academic Press ; 2019 |
|---|---|
| Ausgabe: |
First edition |
| Schlagwörter: |
Diagnostic imaging, Digital techniques HEALTH & FITNESS ; Diseases ; General MEDICAL ; Evidence-Based Medicine Diagnostic imaging ; Digital techniques |
|---|---|
| Formangabe: |
Electronic books |
| Anmerkung: |
Includes bibliographical references and index |
|---|---|
| Umfang: |
1 Online-Ressource (1 online resource) |
| Links: | |
|---|---|
| ISBN: |
0-12-816087-X 0-12-816086-1 978-0-12-816087-9 978-0-12-816086-2 |
| Katalog-ID: |
1734930632 |
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| 245 | 1 | 0 | |a Machine Learning in Bio-Signal Analysis and Diagnostic Imaging |c edited by Nilanjan Dey, Surekha Borra, Amira S. Ashour, Fuqian Shi |
| 250 | |a First edition | ||
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| 650 | 0 | |a Diagnostic imaging |x Digital techniques | |
| 650 | 0 | |a Diseases |x Reporting | |
| 650 | 0 | |a Spectrum analysis | |
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012816087X electronic bk. 0-12-816087-X 0128160861 electronic bk. 0-12-816086-1 9780128160879 electronic bk. 978-0-12-816087-9 9780128160862 electronic bk. 978-0-12-816086-2 (DE-627)1734930632 (DE-599)KEP056069413 (ELSEVIER)on1076873022 (EBP)056069413 DE-627 eng DE-627 rda eng XA-GB RC78.7.D53 COM 000000 bisacsh MED 022000 bisacsh MED 112000 bisacsh MED 014000 bisacsh HEA 039000 bisacsh MED 045000 bisacsh Machine Learning in Bio-Signal Analysis and Diagnostic Imaging edited by Nilanjan Dey, Surekha Borra, Amira S. Ashour, Fuqian Shi First edition London Academic Press [2019] 1 Online-Ressource (1 online resource) Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Includes bibliographical references and index Machine Learning in Bio-Signal Analysis and Diagnostic Imaging presents original research on the advanced analysis and classification techniques of biomedical signals and images that cover both supervised and unsupervised machine learning models, standards, algorithms, and their applications, along with the difficulties and challenges faced by healthcare professionals in analyzing biomedical signals and diagnostic images. These intelligent recommender systems are designed based on machine learning, soft computing, computer vision, artificial intelligence and data mining techniques. Classification and clustering techniques, such as PCA, SVM, techniques, Naive Bayes, Neural Network, Decision trees, and Association Rule Mining are among the approaches presented. The design of high accuracy decision support systems assists and eases the job of healthcare practitioners and suits a variety of applications. Integrating Machine Learning (ML) technology with human visual psychometrics helps to meet the demands of radiologists in improving the efficiency and quality of diagnosis in dealing with unique and complex diseases in real time by reducing human errors and allowing fast and rigorous analysis. The book's target audience includes professors and students in biomedical engineering and medical schools, researchers and engineers Diagnostic imaging Digital techniques Diseases Reporting Spectrum analysis Machine learning Diagnostic imaging Diagnostic Imaging Spectrum Analysis Machine Learning Disease Notification (DNLM)D018563 COMPUTERS ; General HEALTH & FITNESS ; Diseases ; General MEDICAL ; Clinical Medicine MEDICAL ; Diseases MEDICAL ; Evidence-Based Medicine MEDICAL ; Internal Medicine Diagnostic imaging ; Digital techniques Diseases ; Reporting Machine learning Spectrum analysis Diagnostic imaging (OCoLC)fst00892354 Apprentissage automatique (CaQQLa)201-0131435 Imagerie pour le diagnostic (CaQQLa)201-0146124 Imagerie pour le diagnostic - Techniques numériques (CaQQLa)201-0395388 Maladies - Déclaration (CaQQLa)201-0013308 Electronic books Ashour, Amira 1975- herausgeberin edt Shi, Fuqian herausgeberin edt Dey, Nilanjan 1984- herausgeberin edt Borra, Surekha herausgeberin edt 0128160861 9780128160862 Erscheint auch als Druck-Ausgabe 0128160861 9780128160862 https://www.sciencedirect.com/science/book/9780128160862 X:ELSEVIER Verlag lizenzpflichtig GBV-33-Freedom 2022 BSZ-33-EBS-HSAA GBV-33-EBS-MRI GBV-33-EBS-ZHB GBV-33-Freedom 2021 ZDB-33-EBS ZDB-33-EGE 2019 ZDB-33-ESD GBV-33-Freedom 2023 GBV-33-EBS-HST BSZ-33-EBS-C1UB GBV_ILN_105 ISIL_DE-841 SYSFLAG_1 GBV_KXP SSG-OLC-PHA GBV_ILN_132 ISIL_DE-959 GBV_ILN_185 ISIL_DE-Sra5 GBV_ILN_370 ISIL_DE-1373 GBV_ILN_2020 ISIL_DE-Ch1 GBV_ILN_2111 ISIL_DE-944 BO 045F 616.0754 105 01 0841 4074488167 OLR-ELV-TEST Vervielfältigungen (z.B. Kopien, Downloads) sind nur von einzelnen Kapiteln oder Seiten und nur zum eigenen wissenschaftlichen Gebrauch erlaubt. Die Weitergabe an Dritte sowie systematisches Downloaden sind untersagt. Testzugang ZHB Lübeck z 26-02-22 132 01 0959 450000551X EBS Elsevier Vervielfältigungen (z.B. Kopien, Downloads) sind nur von einzelnen Kapiteln oder Seiten und nur zum eigenen wissenschaftlichen Gebrauch erlaubt. Keine Weitergabe an Dritte. Kein systematisches Downloaden durch Robots. Zeitlich begrenzte Lizenzierung k 13-03-24 185 01 3519 451475661X OLR-EBS Vervielfältigungen (z.B. Kopien, Downloads) sind nur von einzelnen Kapiteln oder Seiten und nur zum eigenen wissenschaftlichen Gebrauch erlaubt. Die Weitergabe an Dritte sowie systematisches Downloaden sind untersagt. z 23-04-24 370 01 4370 4540283014 EBS Elsevier Vervielfältigungen (z.B. Kopien, Downloads) sind nur von einzelnen Kapiteln oder Seiten und nur zum eigenen wissenschaftlichen Gebrauch erlaubt. Keine Weitergabe an Dritte. Kein systematisches Downloaden durch Robots. i z 20-06-24 2020 01 DE-Ch1 4520351723 00 --%%-- --%%-- n n Campuslizenz l01 03-05-24 2111 02 DE-944 4046032111 00 --%%-- E-Book Elsevier --%%-- n Elektronischer Volltext - Campuslizenz l01 27-01-22 105 01 0841 https://www.sciencedirect.com/science/book/9780128160862 132 01 0959 Zugriff nur für Angehörige der Hochschule Osnabrück im Hochschulnetz https://www.sciencedirect.com/science/book/9780128160862 185 01 3519 https://www.sciencedirect.com/science/book/9780128160862 370 01 4370 E-Book: Zugriff im HCU-Netz. Zugriff von außerhalb nur für HCU-Angehörige möglich https://www.sciencedirect.com/science/book/9780128160862 2020 01 DE-Ch1 https://www.sciencedirect.com/science/book/9780128160862 2111 02 DE-944 https://www.sciencedirect.com/science/book/9780128160862 132 01 0959 00 EBooks Elsevier Engineering 105 01 0841 OLR-ELV-TEST 132 01 0959 EBS Elsevier 185 01 3519 OLR-EBS 370 01 4370 EBS Elsevier |
| spelling |
012816087X electronic bk. 0-12-816087-X 0128160861 electronic bk. 0-12-816086-1 9780128160879 electronic bk. 978-0-12-816087-9 9780128160862 electronic bk. 978-0-12-816086-2 (DE-627)1734930632 (DE-599)KEP056069413 (ELSEVIER)on1076873022 (EBP)056069413 DE-627 eng DE-627 rda eng XA-GB RC78.7.D53 COM 000000 bisacsh MED 022000 bisacsh MED 112000 bisacsh MED 014000 bisacsh HEA 039000 bisacsh MED 045000 bisacsh Machine Learning in Bio-Signal Analysis and Diagnostic Imaging edited by Nilanjan Dey, Surekha Borra, Amira S. Ashour, Fuqian Shi First edition London Academic Press [2019] 1 Online-Ressource (1 online resource) Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Includes bibliographical references and index Machine Learning in Bio-Signal Analysis and Diagnostic Imaging presents original research on the advanced analysis and classification techniques of biomedical signals and images that cover both supervised and unsupervised machine learning models, standards, algorithms, and their applications, along with the difficulties and challenges faced by healthcare professionals in analyzing biomedical signals and diagnostic images. These intelligent recommender systems are designed based on machine learning, soft computing, computer vision, artificial intelligence and data mining techniques. Classification and clustering techniques, such as PCA, SVM, techniques, Naive Bayes, Neural Network, Decision trees, and Association Rule Mining are among the approaches presented. The design of high accuracy decision support systems assists and eases the job of healthcare practitioners and suits a variety of applications. Integrating Machine Learning (ML) technology with human visual psychometrics helps to meet the demands of radiologists in improving the efficiency and quality of diagnosis in dealing with unique and complex diseases in real time by reducing human errors and allowing fast and rigorous analysis. The book's target audience includes professors and students in biomedical engineering and medical schools, researchers and engineers Diagnostic imaging Digital techniques Diseases Reporting Spectrum analysis Machine learning Diagnostic imaging Diagnostic Imaging Spectrum Analysis Machine Learning Disease Notification (DNLM)D018563 COMPUTERS ; General HEALTH & FITNESS ; Diseases ; General MEDICAL ; Clinical Medicine MEDICAL ; Diseases MEDICAL ; Evidence-Based Medicine MEDICAL ; Internal Medicine Diagnostic imaging ; Digital techniques Diseases ; Reporting Machine learning Spectrum analysis Diagnostic imaging (OCoLC)fst00892354 Apprentissage automatique (CaQQLa)201-0131435 Imagerie pour le diagnostic (CaQQLa)201-0146124 Imagerie pour le diagnostic - Techniques numériques (CaQQLa)201-0395388 Maladies - Déclaration (CaQQLa)201-0013308 Electronic books Ashour, Amira 1975- herausgeberin edt Shi, Fuqian herausgeberin edt Dey, Nilanjan 1984- herausgeberin edt Borra, Surekha herausgeberin edt 0128160861 9780128160862 Erscheint auch als Druck-Ausgabe 0128160861 9780128160862 https://www.sciencedirect.com/science/book/9780128160862 X:ELSEVIER Verlag lizenzpflichtig GBV-33-Freedom 2022 BSZ-33-EBS-HSAA GBV-33-EBS-MRI GBV-33-EBS-ZHB GBV-33-Freedom 2021 ZDB-33-EBS ZDB-33-EGE 2019 ZDB-33-ESD GBV-33-Freedom 2023 GBV-33-EBS-HST BSZ-33-EBS-C1UB GBV_ILN_105 ISIL_DE-841 SYSFLAG_1 GBV_KXP SSG-OLC-PHA GBV_ILN_132 ISIL_DE-959 GBV_ILN_185 ISIL_DE-Sra5 GBV_ILN_370 ISIL_DE-1373 GBV_ILN_2020 ISIL_DE-Ch1 GBV_ILN_2111 ISIL_DE-944 BO 045F 616.0754 105 01 0841 4074488167 OLR-ELV-TEST Vervielfältigungen (z.B. Kopien, Downloads) sind nur von einzelnen Kapiteln oder Seiten und nur zum eigenen wissenschaftlichen Gebrauch erlaubt. Die Weitergabe an Dritte sowie systematisches Downloaden sind untersagt. Testzugang ZHB Lübeck z 26-02-22 132 01 0959 450000551X EBS Elsevier Vervielfältigungen (z.B. Kopien, Downloads) sind nur von einzelnen Kapiteln oder Seiten und nur zum eigenen wissenschaftlichen Gebrauch erlaubt. Keine Weitergabe an Dritte. Kein systematisches Downloaden durch Robots. Zeitlich begrenzte Lizenzierung k 13-03-24 185 01 3519 451475661X OLR-EBS Vervielfältigungen (z.B. Kopien, Downloads) sind nur von einzelnen Kapiteln oder Seiten und nur zum eigenen wissenschaftlichen Gebrauch erlaubt. Die Weitergabe an Dritte sowie systematisches Downloaden sind untersagt. z 23-04-24 370 01 4370 4540283014 EBS Elsevier Vervielfältigungen (z.B. Kopien, Downloads) sind nur von einzelnen Kapiteln oder Seiten und nur zum eigenen wissenschaftlichen Gebrauch erlaubt. Keine Weitergabe an Dritte. Kein systematisches Downloaden durch Robots. i z 20-06-24 2020 01 DE-Ch1 4520351723 00 --%%-- --%%-- n n Campuslizenz l01 03-05-24 2111 02 DE-944 4046032111 00 --%%-- E-Book Elsevier --%%-- n Elektronischer Volltext - Campuslizenz l01 27-01-22 105 01 0841 https://www.sciencedirect.com/science/book/9780128160862 132 01 0959 Zugriff nur für Angehörige der Hochschule Osnabrück im Hochschulnetz https://www.sciencedirect.com/science/book/9780128160862 185 01 3519 https://www.sciencedirect.com/science/book/9780128160862 370 01 4370 E-Book: Zugriff im HCU-Netz. Zugriff von außerhalb nur für HCU-Angehörige möglich https://www.sciencedirect.com/science/book/9780128160862 2020 01 DE-Ch1 https://www.sciencedirect.com/science/book/9780128160862 2111 02 DE-944 https://www.sciencedirect.com/science/book/9780128160862 132 01 0959 00 EBooks Elsevier Engineering 105 01 0841 OLR-ELV-TEST 132 01 0959 EBS Elsevier 185 01 3519 OLR-EBS 370 01 4370 EBS Elsevier |
| allfields_unstemmed |
012816087X electronic bk. 0-12-816087-X 0128160861 electronic bk. 0-12-816086-1 9780128160879 electronic bk. 978-0-12-816087-9 9780128160862 electronic bk. 978-0-12-816086-2 (DE-627)1734930632 (DE-599)KEP056069413 (ELSEVIER)on1076873022 (EBP)056069413 DE-627 eng DE-627 rda eng XA-GB RC78.7.D53 COM 000000 bisacsh MED 022000 bisacsh MED 112000 bisacsh MED 014000 bisacsh HEA 039000 bisacsh MED 045000 bisacsh Machine Learning in Bio-Signal Analysis and Diagnostic Imaging edited by Nilanjan Dey, Surekha Borra, Amira S. Ashour, Fuqian Shi First edition London Academic Press [2019] 1 Online-Ressource (1 online resource) Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Includes bibliographical references and index Machine Learning in Bio-Signal Analysis and Diagnostic Imaging presents original research on the advanced analysis and classification techniques of biomedical signals and images that cover both supervised and unsupervised machine learning models, standards, algorithms, and their applications, along with the difficulties and challenges faced by healthcare professionals in analyzing biomedical signals and diagnostic images. These intelligent recommender systems are designed based on machine learning, soft computing, computer vision, artificial intelligence and data mining techniques. Classification and clustering techniques, such as PCA, SVM, techniques, Naive Bayes, Neural Network, Decision trees, and Association Rule Mining are among the approaches presented. The design of high accuracy decision support systems assists and eases the job of healthcare practitioners and suits a variety of applications. Integrating Machine Learning (ML) technology with human visual psychometrics helps to meet the demands of radiologists in improving the efficiency and quality of diagnosis in dealing with unique and complex diseases in real time by reducing human errors and allowing fast and rigorous analysis. The book's target audience includes professors and students in biomedical engineering and medical schools, researchers and engineers Diagnostic imaging Digital techniques Diseases Reporting Spectrum analysis Machine learning Diagnostic imaging Diagnostic Imaging Spectrum Analysis Machine Learning Disease Notification (DNLM)D018563 COMPUTERS ; General HEALTH & FITNESS ; Diseases ; General MEDICAL ; Clinical Medicine MEDICAL ; Diseases MEDICAL ; Evidence-Based Medicine MEDICAL ; Internal Medicine Diagnostic imaging ; Digital techniques Diseases ; Reporting Machine learning Spectrum analysis Diagnostic imaging (OCoLC)fst00892354 Apprentissage automatique (CaQQLa)201-0131435 Imagerie pour le diagnostic (CaQQLa)201-0146124 Imagerie pour le diagnostic - Techniques numériques (CaQQLa)201-0395388 Maladies - Déclaration (CaQQLa)201-0013308 Electronic books Ashour, Amira 1975- herausgeberin edt Shi, Fuqian herausgeberin edt Dey, Nilanjan 1984- herausgeberin edt Borra, Surekha herausgeberin edt 0128160861 9780128160862 Erscheint auch als Druck-Ausgabe 0128160861 9780128160862 https://www.sciencedirect.com/science/book/9780128160862 X:ELSEVIER Verlag lizenzpflichtig GBV-33-Freedom 2022 BSZ-33-EBS-HSAA GBV-33-EBS-MRI GBV-33-EBS-ZHB GBV-33-Freedom 2021 ZDB-33-EBS ZDB-33-EGE 2019 ZDB-33-ESD GBV-33-Freedom 2023 GBV-33-EBS-HST BSZ-33-EBS-C1UB GBV_ILN_105 ISIL_DE-841 SYSFLAG_1 GBV_KXP SSG-OLC-PHA GBV_ILN_132 ISIL_DE-959 GBV_ILN_185 ISIL_DE-Sra5 GBV_ILN_370 ISIL_DE-1373 GBV_ILN_2020 ISIL_DE-Ch1 GBV_ILN_2111 ISIL_DE-944 BO 045F 616.0754 105 01 0841 4074488167 OLR-ELV-TEST Vervielfältigungen (z.B. 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Ashour, Fuqian Shi First edition London Academic Press [2019] 1 Online-Ressource (1 online resource) Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Includes bibliographical references and index Machine Learning in Bio-Signal Analysis and Diagnostic Imaging presents original research on the advanced analysis and classification techniques of biomedical signals and images that cover both supervised and unsupervised machine learning models, standards, algorithms, and their applications, along with the difficulties and challenges faced by healthcare professionals in analyzing biomedical signals and diagnostic images. These intelligent recommender systems are designed based on machine learning, soft computing, computer vision, artificial intelligence and data mining techniques. Classification and clustering techniques, such as PCA, SVM, techniques, Naive Bayes, Neural Network, Decision trees, and Association Rule Mining are among the approaches presented. The design of high accuracy decision support systems assists and eases the job of healthcare practitioners and suits a variety of applications. Integrating Machine Learning (ML) technology with human visual psychometrics helps to meet the demands of radiologists in improving the efficiency and quality of diagnosis in dealing with unique and complex diseases in real time by reducing human errors and allowing fast and rigorous analysis. 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Kein systematisches Downloaden durch Robots. i z 20-06-24 2020 01 DE-Ch1 4520351723 00 --%%-- --%%-- n n Campuslizenz l01 03-05-24 2111 02 DE-944 4046032111 00 --%%-- E-Book Elsevier --%%-- n Elektronischer Volltext - Campuslizenz l01 27-01-22 105 01 0841 https://www.sciencedirect.com/science/book/9780128160862 132 01 0959 Zugriff nur für Angehörige der Hochschule Osnabrück im Hochschulnetz https://www.sciencedirect.com/science/book/9780128160862 185 01 3519 https://www.sciencedirect.com/science/book/9780128160862 370 01 4370 E-Book: Zugriff im HCU-Netz. Zugriff von außerhalb nur für HCU-Angehörige möglich https://www.sciencedirect.com/science/book/9780128160862 2020 01 DE-Ch1 https://www.sciencedirect.com/science/book/9780128160862 2111 02 DE-944 https://www.sciencedirect.com/science/book/9780128160862 132 01 0959 00 EBooks Elsevier Engineering 105 01 0841 OLR-ELV-TEST 132 01 0959 EBS Elsevier 185 01 3519 OLR-EBS 370 01 4370 EBS Elsevier |
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Ashour, Fuqian Shi First edition London Academic Press [2019] 1 Online-Ressource (1 online resource) Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Includes bibliographical references and index Machine Learning in Bio-Signal Analysis and Diagnostic Imaging presents original research on the advanced analysis and classification techniques of biomedical signals and images that cover both supervised and unsupervised machine learning models, standards, algorithms, and their applications, along with the difficulties and challenges faced by healthcare professionals in analyzing biomedical signals and diagnostic images. These intelligent recommender systems are designed based on machine learning, soft computing, computer vision, artificial intelligence and data mining techniques. Classification and clustering techniques, such as PCA, SVM, techniques, Naive Bayes, Neural Network, Decision trees, and Association Rule Mining are among the approaches presented. The design of high accuracy decision support systems assists and eases the job of healthcare practitioners and suits a variety of applications. Integrating Machine Learning (ML) technology with human visual psychometrics helps to meet the demands of radiologists in improving the efficiency and quality of diagnosis in dealing with unique and complex diseases in real time by reducing human errors and allowing fast and rigorous analysis. 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Machine Learning in Bio-Signal Analysis and Diagnostic Imaging presents original research on the advanced analysis and classification techniques of biomedical signals and images that cover both supervised and unsupervised machine learning models, standards, algorithms, and their applications, along with the difficulties and challenges faced by healthcare professionals in analyzing biomedical signals and diagnostic images. These intelligent recommender systems are designed based on machine learning, soft computing, computer vision, artificial intelligence and data mining techniques. Classification and clustering techniques, such as PCA, SVM, techniques, Naive Bayes, Neural Network, Decision trees, and Association Rule Mining are among the approaches presented. The design of high accuracy decision support systems assists and eases the job of healthcare practitioners and suits a variety of applications. Integrating Machine Learning (ML) technology with human visual psychometrics helps to meet the demands of radiologists in improving the efficiency and quality of diagnosis in dealing with unique and complex diseases in real time by reducing human errors and allowing fast and rigorous analysis. The book's target audience includes professors and students in biomedical engineering and medical schools, researchers and engineers Includes bibliographical references and index |
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