Pancreatic Cancer Segmentation and Classification in CT Imaging using Antlion Optimization and Deep Learning Mechanism

Pancreatic cancer, a fatal type of cancer, has a very poor prognosis. To monitor, forecast, and categorise cancer presence, automated pancreatic cancer segmentation and classification utilising Computer-Aided Diagnostic (CAD) model can be used. Furthermore, deep learning algorithms can provide in-depth diagnostic knowledge and precise image analysis for therapeutic usage. In this context, our study aims to develop an Antlion Optimization-Convolutional Neural Network-Gated Recurrent Unit (ALO-CNN-GRU) model for pancreatic tumor segmentation and classification based on deep learning and CT scans. The ALO-CNN-GRU technique's objective is to segment and categorize the presence of cancer tissues. This technique consists of pre-processing, segmentation and feature extraction and classification phases. The images go through a pre-processing stage to reduce noise from the dataset that was obtained. A hybrid Gaussian and median filter is applied for the pre-processing phase. To identify the pancreatic area that is affected, the segmentation is processed utilizing the Antlion optimization method. Then, the categorization of pancreatic cancer as benign or malignant is done by employing the classifiers of the Convolutional neural network and Gated Recurrent Unit networks. The suggested model offers improved precision and a better rate of pancreatic cancer diagnosis with an accuracy of 99.92%.