The pancreatic cancer (PC) has the lowermost survivability and ranks as the fourth prominent reason of cancer death and the rate of death is increasedby ever year. The major risk factors for the invasion of pancreatic cancer are smoking, intake of alcohol, diabetes mellitus and earlier pancreatitis also leads to the development of pancreatic cancer. The aim of this suggested method is to detect the PC, which is accomplished by using the image processing technique. In this paper, the CT image is considered as the input image which experiences the process of preprocessing to eliminate the presence of noise in the image and that has been accomplished by adaptive Weiner filter. After the preprocessing, the noise free image is then segmented by using the modified region grow model. SIFT (Scale invariant feature transform) method is used to extract the parameters of the pancreatic cancer and this extracted features are enhanced by PCA (principal component analysis) to optimize the features of the pancreatic CT image. The parameters of the image have been stimulated by relating the CNN (convolutional neural network) classifier. After classification, the classified image has been compared, thus the comparison is done only between the test data and the trained data to categorize the image as PC or non-PC. The entire process is then stimulated in the MATLAB and most recent technique is used for the performance estimation and so high accuracy is achieved.