Lung Cancer detection and Classification by using Machine Learning & Multinomial Bayesian

Abstract

Image Processing is a technique to enhance raw images received from cameras/sensors placed on satellites, space probes and aircrafts or pictures taken in normal day-today life for various applications. Various techniques have been developed in Image processing during the last four to five decades. Most of the techniques are developed for enhancing images obtained from unmanned spacecrafts, space probes and military reconnaissance flights. Image Processing systems are becoming popular due to easy availability of powerful personnel computers, large size memory devices, graphics software etc. Medical image segmentation & classification play an important role in medical research field. The patient CT lung images are classified into normal and abnormal category. Then, the abnormal images are subjected to segmentation to view the tumor portion. Classification depends on the features extracted from the images. We mainly are concentrating on feature extraction stage to yield better classification performance. Texture based features such as GLCM (Gray Level Co-occurrence Matrix) features play an important role in medical image analysis. Totally 12 different statistical features were extracted. To select the discriminative features among them we use sequential forward selection algorithm. Afterwards we prefer multinomial multivariate Bayesian for the classification stage. Classifier performance will be analyses further. The effectiveness of the modified weighted FCM algorithm in terms of computational rate is improved by modifying the cluster center and membership value updating criterion. Objective of this paper is that To achieve a perfect classification by multivariate multinomial Bayesian