Lung cancer seems to be the common cause of death among people throughout the world. Early detection of lung cancer can increase the chance of survival among people. The overall 5-year survival rate for lung cancer patients increases from 14 to 49% if the disease is detected in time. Although Computed Tomography (CT) can be more efficient than X-ray. However, problem seemed to merge due to time constraint in detecting the present of lung cancer regarding on the several diagnosing method used. Hence, a lung cancer detection system using image processing is used to classify the present of lung cancer in an CT- images. This paper is aiming to get the more accurate results by using particle sWarm optimization and segmentation techniques. The paper proposes that the images are pre-processed and features are extracted by linear binary pattern based feature extraction technique, then that extracted features are selected by applying genetic algorithm and particle swarm optimisation which selects the top ranked features..The CT findings denote what radiologists see in CT scans for diagnosing diseases, which are also often called CT features or CT manifestation. Thus the problem of automatic classification of CT findings of lung lesions in CT scans can be diagnosed.
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