Inhaltsangabe
In different cancers, for example, lung cancer, the time calculated is imperative to find the anomaly issue in target images. Gray Level Co-event Matrix (GLCM) is utilized for preprocessing of images and to feature extraction procedures to check the condition of the patient whether it is ordinary or irregular. Surface-based elements, for example, GLCM features assume a vital part of remedial image examination which is utilized for the identification of Lung cancer. In the event that lung cancer is effectively distinguished and anticipated in its initial stages, it lessens numerous treatment choices and furthermore, decreases the danger of intrusive surgery and increase survival rate. The proposed method will efficiently identify the position of the tumor in lungs using the probability framework. This will offer a promising outcome for recognition and diagnosis of lung cancer. In the proposed work, GLCM features are used for the prediction of lung tumor and tests are performed for performance analysis in comparison with the histogram and GLCM features, in which GLCM features are accurate in predicting lung tumor even if it takes more time than histogram features.
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Über die Autorin bzw. den Autor
Dr. B. V. Ramana Reddy completed B. Tech, M. Tech and Ph.D degrees from S.V. University, JNT University Hyderabad and J N T University Anantapur in 1991, 2002 and 2011 respectively. Currently he is working as a Professor in Computer Science Engineering, K S R M College of Engineering, Kadapa, Andhra Pradesh, India.
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Taschenbuch. Zustand: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -In different cancers, for example, lung cancer, the time calculated is imperative to find the anomaly issue in target images. Gray Level Co-event Matrix (GLCM) is utilized for preprocessing of images and to feature extraction procedures to check the condition of the patient whether it is ordinary or irregular. Surface-based elements, for example, GLCM features assume a vital part of remedial image examination which is utilized for the identification of Lung cancer. In the event that lung cancer is effectively distinguished and anticipated in its initial stages, it lessens numerous treatment choices and furthermore, decreases the danger of intrusive surgery and increase survival rate. The proposed method will efficiently identify the position of the tumor in lungs using the probability framework. This will offer a promising outcome for recognition and diagnosis of lung cancer. In the proposed work, GLCM features are used for the prediction of lung tumor and tests are performed for performance analysis in comparison with the histogram and GLCM features, in which GLCM features are accurate in predicting lung tumor even if it takes more time than histogram features. 152 pp. Englisch. Bestandsnummer des Verkäufers 9786202514347
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Zustand: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Ramana Reddy Dr. B. V.Dr. B. V. Ramana Reddy completed B. Tech, M. Tech and Ph.D degrees from S.V. University, JNT University Hyderabad and J N T University Anantapur in 1991, 2002 and 2011 respectively. Currently he is working as a . Bestandsnummer des Verkäufers 409976851
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Taschenbuch. Zustand: Neu. This item is printed on demand - Print on Demand Titel. Neuware -In different cancers, for example, lung cancer, the time calculated is imperative to find the anomaly issue in target images. Gray Level Co-event Matrix (GLCM) is utilized for preprocessing of images and to feature extraction procedures to check the condition of the patient whether it is ordinary or irregular. Surface-based elements, for example, GLCM features assume a vital part of remedial image examination which is utilized for the identification of Lung cancer. In the event that lung cancer is effectively distinguished and anticipated in its initial stages, it lessens numerous treatment choices and furthermore, decreases the danger of intrusive surgery and increase survival rate. The proposed method will efficiently identify the position of the tumor in lungs using the probability framework. This will offer a promising outcome for recognition and diagnosis of lung cancer. In the proposed work, GLCM features are used for the prediction of lung tumor and tests are performed for performance analysis in comparison with the histogram and GLCM features, in which GLCM features are accurate in predicting lung tumor even if it takes more time than histogram features.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 152 pp. Englisch. Bestandsnummer des Verkäufers 9786202514347
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Predicting Lung Cancer Using Machine Learning Techniques | A Detailed Study
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Taschenbuch. Zustand: Neu. Predicting Lung Cancer Using Machine Learning Techniques | A Detailed Study | B. V. Ramana Reddy | Taschenbuch | Einband - flex.(Paperback) | Englisch | 2020 | LAP LAMBERT Academic Publishing | EAN 9786202514347 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu. Bestandsnummer des Verkäufers 119169898
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Predicting Lung Cancer Using Machine Learning Techniques : A Detailed Study
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Taschenbuch. Zustand: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - In different cancers, for example, lung cancer, the time calculated is imperative to find the anomaly issue in target images. Gray Level Co-event Matrix (GLCM) is utilized for preprocessing of images and to feature extraction procedures to check the condition of the patient whether it is ordinary or irregular. Surface-based elements, for example, GLCM features assume a vital part of remedial image examination which is utilized for the identification of Lung cancer. In the event that lung cancer is effectively distinguished and anticipated in its initial stages, it lessens numerous treatment choices and furthermore, decreases the danger of intrusive surgery and increase survival rate. The proposed method will efficiently identify the position of the tumor in lungs using the probability framework. This will offer a promising outcome for recognition and diagnosis of lung cancer. In the proposed work, GLCM features are used for the prediction of lung tumor and tests are performed for performance analysis in comparison with the histogram and GLCM features, in which GLCM features are accurate in predicting lung tumor even if it takes more time than histogram features. Bestandsnummer des Verkäufers 9786202514347
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