Verwandte Artikel zu Detection of Lung Carcinoma Using Fuzzy and ACO Techniques
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Lung Carcinoma or cancer - the most dangerous disease in the world is caused by the multiplication of cells which eventually grows into tumors. Lung carcinoma is one of the most dangerous cancer types in the world. These diseases can spread in the body by uncontrolled cell growth in the tissues of the lungs. Early detection of cancer can save the life and survivability of patients who are affected by lung carcinoma. In the presented work, lung carcinoma has been detected by using Fuzzy and ACO techniques. Lung carcinoma is detected on the basis of features of the Dicom image of the lungs. In this system, we use a histogram equalizer at the preprocessing unit. Then Binarization approach and Grey Level Co-occurrence Matrix approach are used for features extraction of the Dicom image. We create a database of features and design the rules set of fuzzy in the training part. At testing, part Dicom images of lungs are uploaded. The same features are extracted of the image and then compare with the features of the database. In the classification stage, a combination of two classification methods namely FUZZY and ACO are used.
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Detection of Lung Carcinoma Using Fuzzy and ACO Techniques
Verlag:
LAP LAMBERT Academic Publishing, 2022
ISBN 10: 6205511851
ISBN 13: 9786205511855
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Detection of Lung Carcinoma Using Fuzzy and ACO Techniques
Verlag:
LAP LAMBERT Academic Publishing Okt 2022, 2022
ISBN 10: 6205511851
ISBN 13: 9786205511855
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Taschenbuch. Zustand: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Lung Carcinoma or cancer - the most dangerous disease in the world is caused by the multiplication of cells which eventually grows into tumors. Lung carcinoma is one of the most dangerous cancer types in the world. These diseases can spread in the body by uncontrolled cell growth in the tissues of the lungs. Early detection of cancer can save the life and survivability of patients who are affected by lung carcinoma. In the presented work, lung carcinoma has been detected by using Fuzzy and ACO techniques. Lung carcinoma is detected on the basis of features of the Dicom image of the lungs. In this system, we use a histogram equalizer at the preprocessing unit. Then Binarization approach and Grey Level Co-occurrence Matrix approach are used for features extraction of the Dicom image. We create a database of features and design the rules set of fuzzy in the training part. At testing, part Dicom images of lungs are uploaded. The same features are extracted of the image and then compare with the features of the database. In the classification stage, a combination of two classification methods namely FUZZY and ACO are used. 76 pp. Englisch. Bestandsnummer des Verkäufers 9786205511855
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Beispielbild für diese ISBN
Detection of Lung Carcinoma Using Fuzzy and ACO Techniques
Verlag:
LAP LAMBERT Academic Publishing, 2022
ISBN 10: 6205511851
ISBN 13: 9786205511855
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Detection of Lung Carcinoma Using Fuzzy and ACO Techniques
Verlag:
LAP LAMBERT Academic Publishing, 2022
ISBN 10: 6205511851
ISBN 13: 9786205511855
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Detection of Lung Carcinoma Using Fuzzy and ACO Techniques
Verlag:
LAP LAMBERT Academic Publishing, 2022
ISBN 10: 6205511851
ISBN 13: 9786205511855
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Softcover
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Zustand: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Saini Sandeep KumarMr. Sandeep Kumar Saini has 10 years of experience in research and academics. He published more than 20 research papers in peer-reviewed journals and conferences. His research area is Artificial Intelligence and ne. Bestandsnummer des Verkäufers 755870552
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Detection of Lung Carcinoma Using Fuzzy and ACO Techniques
Verlag:
LAP LAMBERT Academic Publishing Okt 2022, 2022
ISBN 10: 6205511851
ISBN 13: 9786205511855
Neu
Taschenbuch
Anbieter: buchversandmimpf2000, Emtmannsberg, BAYE, Deutschland
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Taschenbuch. Zustand: Neu. Neuware -Lung Carcinoma or cancer - the most dangerous disease in the world is caused by the multiplication of cells which eventually grows into tumors. Lung carcinoma is one of the most dangerous cancer types in the world. These diseases can spread in the body by uncontrolled cell growth in the tissues of the lungs. Early detection of cancer can save the life and survivability of patients who are affected by lung carcinoma. In the presented work, lung carcinoma has been detected by using Fuzzy and ACO techniques. Lung carcinoma is detected on the basis of features of the Dicom image of the lungs. In this system, we use a histogram equalizer at the preprocessing unit. Then Binarization approach and Grey Level Co-occurrence Matrix approach are used for features extraction of the Dicom image. We create a database of features and design the rules set of fuzzy in the training part. At testing, part Dicom images of lungs are uploaded. The same features are extracted of the image and then compare with the features of the database. In the classification stage, a combination of two classification methods namely FUZZY and ACO are used.Books on Demand GmbH, Überseering 33, 22297 Hamburg 76 pp. Englisch. Bestandsnummer des Verkäufers 9786205511855
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Detection of Lung Carcinoma Using Fuzzy and ACO Techniques
Verlag:
LAP LAMBERT Academic Publishing, 2022
ISBN 10: 6205511851
ISBN 13: 9786205511855
Neu
Taschenbuch
Anbieter: AHA-BUCH GmbH, Einbeck, Deutschland
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Taschenbuch. Zustand: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Lung Carcinoma or cancer - the most dangerous disease in the world is caused by the multiplication of cells which eventually grows into tumors. Lung carcinoma is one of the most dangerous cancer types in the world. These diseases can spread in the body by uncontrolled cell growth in the tissues of the lungs. Early detection of cancer can save the life and survivability of patients who are affected by lung carcinoma. In the presented work, lung carcinoma has been detected by using Fuzzy and ACO techniques. Lung carcinoma is detected on the basis of features of the Dicom image of the lungs. In this system, we use a histogram equalizer at the preprocessing unit. Then Binarization approach and Grey Level Co-occurrence Matrix approach are used for features extraction of the Dicom image. We create a database of features and design the rules set of fuzzy in the training part. At testing, part Dicom images of lungs are uploaded. The same features are extracted of the image and then compare with the features of the database. In the classification stage, a combination of two classification methods namely FUZZY and ACO are used. Bestandsnummer des Verkäufers 9786205511855
Anzahl: 1 verfügbar