Paper Contents
Abstract
Abstract Rarely is pancreatic cancer discovered in its early stages, when it is most treatable. This is because, in many cases, symptoms do not appear until the disease has progressed to other organs. Treatment options for pancreatic cancer are selected according to the cancer\'s stage. Options might be radiation treatment, chemotherapy, surgery, or a mix of these. Cancers have fuzzy borders and tiny size, making them challenging to manually annotate and automatically segment. That is why cancer prediction is so important. The convolutional neural network classifier proposed in this study is intended to identify pancreatic cancer. The segmentation method used in the test, Fuzzy C Means, divides the picture into segments. The Gabor based Region Covariance Matrix (GRCM) is used to extract features, and the GW optimization method is used to optimize the process. Using a powerful classifier a Grey wolf Optimization based Convolutional Neural Networks (GWO-based CNN), the outcome is correctly predicted. The findings acquired through the use of CNN Classifier were precise. MATLAB simulation software is used in the implementation of this project. The Accuracy Comparison of the GWO-CNN is 92.5% and Specificity Comparison of GWO-CNN is 93% respectively.
Copyright
Copyright © 2026 Sowmya M. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
