In this paper, we represent computer aided diagnosis (CAD) system for recognition of lung cancer by analyzing CT images of chest. CAD system helps to improve the diagnostic performance of radiologists in their image interpretations. The proposed system relies on three stages mainly; firstly, the CT image is enhanced. Secondly, the lung and tumor are segmented from the input CT image by separating them from other organs in the CT scan. This is done using region growing Algorithm for segmenting the lung parenchyma and a set of morphological operations to detect the tumor. Thirdly, the geometrical information and transformed based features such as Radon transform based features obtained from the extracted tumor are used to classify the lung tumor into benign and malignant employing adaptive neuro fuzzy inference system (ANFIS) classifier. Correct Classification rate of 98% is obtained by using geometric features.

In this paper, computed tomographic (CT) chest images were investigated to develop an automated system to discriminate lung cancer. These were done by analyzing Data recorded for patients with benign cancer, and also patients with malignant lung cancer were taken in account. The techniques for utilized feature extraction included features derived from texture analysis based on Gray Level Co-occurrence Matrix (GLCM) of the input image, as well as features derived from curvelet transform-based features. An artificial neural network (ANN) with radial basis function classifier was utilized to classify the type of cancer whether benign or malignant. The results have shown that using curvelet domain features gives the highest rate to recognize lung cancer. Classification correct rate is up to 96%. KeywordsCurvelet transform, lung cancer, image processing, ANN with radial basis function. Negative(Benign)1