PANCREATIC CANCER DETECTION USING CONVOLUTIONAL NEURAL NETWORK

PANCREATIC CANCER DETECTION USING CONVOLUTIONAL NEURAL NETWORK
Author(s):
Ferdin Meriya Benitta, Dr. A. Lourdes Mary
Keywords:
PSO-CNN, wiener filter, FCM clustering, GLCM.
Abstract
One of the worst cancer diagnoses that a patient endures is pancreatic cancer, which has a very low 5 year chance of surviving. The primary cause of most cases of this syndrome is pancreatic cancer. Many cancer sufferers are now able to spot aberrations at the beginning of the disease because of medical image scans. It is challenging to spread the technology because of the high cost of the required infrastructure and equipment, which keeps it out of many people's price ranges. In this paper, image processing and PSO-CNN are used to identify pancreatic cancer in images. The Wiener reduction filter is used to remove noise from pictures within the image preparation stage. The Fuzzy C- means (FCM) algorithm divides the image into its individual parts using an organizing method. Image segmentation facilitates the process of detecting objects in an image while recognizing the areas of concern. To extract essential data from digital photos, the GLCM approach is used. The classification is carried out using the techniques PSO- CNN, naive Bayes, and AdaBoost. The PSO-CNN algorithm has higher precision, sensitivity, and specificity.
Article Details
Unique Paper ID: 161177 Publication Volume & Issue: Volume 10, Issue 2 Page(s): 898 - 903
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