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

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