Detection of Lung Carcinoma Using Fuzzy and ACO Techniques - Saini, Sandeep Kumar
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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…mehr

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Produktbeschreibung
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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Autorenporträt
Mr. 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 neural networks.