Detection of Lung Carcinoma Using Fuzzy and ACO Techniques

Sandeep Kumar Saini

ISBN 10: 6205511851 / ISBN 13: 9786205511855

Verlag: LAP LAMBERT Academic Publishing, 2022

Beschreibung

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

Bibliografische Details

• Titel: Detection of Lung Carcinoma Using Fuzzy and ACO Techniques
• Verlag: LAP LAMBERT Academic Publishing
• Erscheinungsjahr: 2022
• Sprache: Englisch
• ISBN-10: 6205511851
• ISBN-13: 9786205511855
• Einband: Taschenbuch
• Zustand: Neu
• Abmessungen: 220B x 150 H x 5 T Millimeter Breite: 220 mm, Höhe: 150 mm, Tiefe: 5 mm
• Gewicht: 131 Gramm

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Inhaltsangabe

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.