Grey Wolf Optimizer Based Deep Learning for Pancreatic Nodule Detection

Abstract

At an early point, the diagnosis of pancreatic cancer is mediocre, since the radiologist is skill deficient. Serious threats have been posed due to the above reasons, hence became mandatory for the need of skilled technicians. However, it also became a time-consuming process. Hence the need for automated diagnosis became mandatory. In order to identify the tumor accurately, this research pro-poses a novel Convolution Neural Network (CNN) based superior image classification technique. The proposed deep learning classification strategy has a precision of 97.7%, allowing for more effective usage of the automatically exe-cuted feature extraction technique to diagnose cancer cells. Comparative analysis with CNN-Grey Wolf Optimization (GWO) is carried based on varied testing and training outcomes. The suggested study is carried out at a rate of 90%–10%, 80%–20%, and 70%–30%, indicating the robustness of the proposed research work. Outcomes show that the suggested method is effective. GWO-CNN is reli-able and accurate relative to other detection methods available in the literatures.