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 proposes 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 executed 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 reliable and accurate relative to other detection methods available in the literatures.

Optimizing Path Planning for Smart Vehicles: A Comprehensive Review of Metaheuristic Algorithms

“…GWO has been applied to solve optimization problems in a diverse range of fields. In medicine, it has been used for various optimization tasks [143,144]. In manufacturing, it has been employed for operation sequencing [145].…”