PREDICTION OF LUNG CANCER USING FUZZY INFERENCE SYSTEM

The most leading cancer among all in human beings is lung cancer. There are more than 17% percent of the total cancer related deaths so the fast and early detection of lung cancer can help in a sharp decrease in the lung cancer death rate. The CT image of lung helps us to find the presence of lung cancer. Doctor analyses the CT image and predicts the presence of cancer nodule. There is a chances of false recognition in the manual identification of cancer. So there is a need of automated approach of lung cancer detection Image processing technique can be used for this purpose. In this paper we propose a Lung cancer identification system that uses a fuzzy inference system to spot the most prominent cancer cells. The approach has four stage to detect the existence of cancer nodule in lung. Pre-processing stage, Segmentation stage, feature extraction stage and fuzzy inference rules to identify lung cells. Pre-processing step includes image enhancement. Enhanced CT image of lung is then passed through segmentation phase. From the segmented output features are extracted to predict the existence of abnormality of lung. On these extracted features fuzzy rules are applied to identify the possibility of cancer cells