Detection Lung Carcinoma Using by Saini Sandeep (8 results)

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Taschenbuch. Condition: 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.

Taschenbuch. Condition: Neu. Detection of Lung Carcinoma Using Fuzzy and ACO Techniques | Sandeep Kumar Saini | Taschenbuch | Englisch | 2022 | LAP LAMBERT Academic Publishing | EAN 9786205511855 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu.

Taschenbuch. Condition: 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.

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Condition: 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.

Taschenbuch. Condition: 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.