Abstract
Early-stage pancreatic cancer is highly curable, although it is uncommon to find. This is because symptoms often don’t show up until the disease has spread to other organs. The treatment choices for pancreatic cancer are determined on the extent of the malignancy. Options include surgery, chemotherapy, radiation therapy, or a combination of these. Nevertheless it is are difficult to separate and manually classify because of their small size and hazy boundaries. This study proposes to identify pancreatic cancer by utilizing an optimized convolutional neural network (CNN) classifier. The test image is segmented using the Fuzzy C Means segmentation approach. The Grey Wolf Optimization GWO algorithm is used to optimize the procedure after features are extracted using Gabor based Region Covariance Matrix Method. The result is accurately predicted owing to strong classifier (a GWO-based CNN). The proposed work is implemented using simulation tools from MATLAB.
Published Version
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