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Identification of CT Lung Tumor Using Fuzzy Clustering Algorithm

    Jalal deen K Karthigai Priya G Magesh B Kubendran R

International Research Journal on Advanced Science Hub, 2021, Volume 3, Issue Special Issue ICEST 1S, Pages 30-33
10.47392/irjash.2021.016

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Abstract

The main principle for the system-based study of lung cancers in CT images is cancer cell recognition and segmentation. Anyhow, in low-contrast pictures, it is a complex job as the low-level images are too small to detect. We are proposing a new technique in this project for the automated detection of lung cancers. Alternatively, by probability density function estimation, we enhance the intensity contrast of CT images. We use the expectation maximization / maximization of the posterior marginal to find cancerous areas. Finally, to decrease noise and classify focal cancers, we use shape limitation. The resolution of more than 95 percent of this fuzzy-based segmentation method is achieved and 9 percent accuracy is also given.
Keywords:
    Fuzzy C-means CT scan CNN Expectation maximization/Maximization neural network
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(2021). Identification of CT Lung Tumor Using Fuzzy Clustering Algorithm. International Research Journal on Advanced Science Hub, 3(Special Issue ICEST 1S), 30-33. doi: 10.47392/irjash.2021.016
Jalal deen K; Karthigai Priya G; Magesh B; Kubendran R. "Identification of CT Lung Tumor Using Fuzzy Clustering Algorithm". International Research Journal on Advanced Science Hub, 3, Special Issue ICEST 1S, 2021, 30-33. doi: 10.47392/irjash.2021.016
(2021). 'Identification of CT Lung Tumor Using Fuzzy Clustering Algorithm', International Research Journal on Advanced Science Hub, 3(Special Issue ICEST 1S), pp. 30-33. doi: 10.47392/irjash.2021.016
Identification of CT Lung Tumor Using Fuzzy Clustering Algorithm. International Research Journal on Advanced Science Hub, 2021; 3(Special Issue ICEST 1S): 30-33. doi: 10.47392/irjash.2021.016
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