Proceedings of VESCOMM-2016 4 th NATIONAL CONFERENCE ON “RECENT TRENDES IN VLSI, EMBEDED SYSTEM, SIGNAL PROCESSING AND COMMUNICATION In Association with “The Institution of Engineers (India)” and IJRPET February 12th, 2016 Organized by Department of Electronics and Telecommunication, V.V.P.I.E.T, Solapur Paper ID: VESCOMM29 LUNG CANCER DETECTION SYSTEM BY USING BAYESIAN CLASSIFIER BhagyarekhaU.Dhaware Electronicsand Telecommunication Department SKN Sinhgad College of Engg.,KortiPandharpur,India Email:bhagyarekha.dhaware28@gmail.com Anjali C. Pise Electronicsand Telecommunication Department SKN SinhgadCollege of Engg.,Korti,Pandharpur,India Email: prof_anjali@rediffmail.com Abstract :Medical image enhancement & classification play an important role in medical research area. To analyse the texture Computed Tomography (CT) images of lungs are taken to find the values of various parameters of texture. Mainly CT lung images are classified into normal and abnormal category. Classification of images depends on the features extracted from the images. Proposed system focusing on texture based features such as GLCM (Gray Level Co- occurrence Matrix) feature plays an important role in medical image analysis. Totally 12 different statistical features & 7 shape features will be extracted. To select the required features among them, use sequential forward selection algorithm. Afterwards prefer Bayesian classifier for the classification stage which gives perfect classification.. Keywords—LCD,CLAHE,GLCM,CDF,SFA, Bayesian Classifier,Texture Feature Extraction,Lung Cancer Detection System,CT images,IP, MATLAB, 1.INTRODUCTION Lung cancer is considered to be the main cause of cancer death worldwide, and it is difficult to detect in its early stages because symptoms appear only at advanced stages causing the mortality rate to be the highest among all other types of cancer [1]. More people die because of lung cancer than any other types of cancer such as: breast, colon, and prostate cancers. There is significant evidence indicating that the early detection of lung cancer will decrease the mortality rate. The most recent estimates according to the latest statistics provided by world health organization indicates that around 7.6 million deaths worldwide each year because of this type of cancer. Furthermore, mortality from cancer are expected to continue rising, to become around 17 million worldwide in 2030[1].The early detection of lung cancer in its primary stage is a challenging problem, due to the complicated structure of the cancer cells, where most of the cells are overlapped to each other. It is a computational procedure that sort images into groups according to their similarities. use Histogram Equalization for preprocessing of the images and feature extraction process and multivariate multinomial Bayesian classifier to check the state of a patient in its early stage whether it is normal or abnormal. The manual analysis of the sputum samples is a very time consuming, inaccurate and requires well trained person to avoid diagnostic errors. The quantitative procedure is very helpful for earlier detection of lung cancer. Experimental analysis will be made with dataset to evaluate the performance of the different classifiers. The performance is based on the correct and incorrect classification of the classifier. Preprocess the given test image for reducing noise and to enhance the contrast by using Contrast Limited Adaptive Histogram Equalization (CLAHE). Afterwards, texture features will be extracted from the test image using GLCM. In feature extraction stage, statistical measurements will be calculated from the gray level co-occurrence matrix for different directions and distances. Among the various features extracted select the distinct features that will be utilized for classification purpose. For the selection of features SFS (Sequential Forward Selection) is used. Bayesian classifier is used to classify whether the test image comes under normal or abnormal. The proposed methodology consists of a set of stages startingfrom collecting Lung CT images. The main steps are shown below. Fig. 1: General Block diagram of LCD System Preprocessing Feature Extraction Classification Input Image Result 1