Lung Cancer Prediction using Fuzzy Inference System

Now a day’s lung cancer is the leading cancer among all human beings. Early detection of lung cancer can help in a sharp decrease in the lung cancer death rate, which accounts for more than 17% percent of the total cancer related deaths. Presence of lung cancer can be identified with the help of a CT image of lung. Doctor analyses the CT image and predicts the presence of cancer nodule. This manual identification may have the chances for false recognition. 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.