Lung cancer detection using Bayasein classifier and FCM segmentation

Image enhancement and classification is a big task, especially while performing in medical field. Enhancing and image classification is used for analysis of texture computed tomography (CT). In this paper images of lungs were taken for find various parameters of the texture. Mainly CT images of lungs can be categorised into normal and abnormal. Classification is based on the features extracted from the taken image. Implementation of the system focuses on texture based features e.g. GLCM (gray level co-occurrence matrix) feature plays an vital role in medical field. Selection is based on the twelve various statistical features and seven shape for extraction by applying sequential forward selection algorithm. After application of sequential forward selection algorithm Bayesian classifier was applied among classified data to get best classification.