Integration of Grey Wolf Optimization Framework with Convolutional Neural Network for Kidney Tumor Classification from Ct Images
R.Mary Victoria · Communications on Applied Nonlinear Analysis · 2025
Our society is affected by the most common disease kidney tumor (KT) in humans. The early diagnosis of KT may reduce the risk of death rates. Preventive measures can be taken to reduce the severe effects and overcome the tumor progression. Traditional methods consume time and tedious task. Deep Learning (DL) methods are now popular in constructing the systems that can detect and classify KT accurately in short duration of time. DL uses pre-trained Convolutional Neural Network (CNN) techniques to provide analysis from medical images. In this paper, Grey Wolf Optimization (GWO) algorithm optimizes CNN hyperparameters. The performance metric values for the detection models of CNN, CNN combined with particle swarm optimization (PSO), CNN combined with genetic algorithm (GA), CNN combined with Search Engine Optimization (SEO), CNN combined with Wild Horse Optimization (WHO) are compared with the propose CNN combined with GWO. The proposed model achieved promising results when the number of classes able to be predicted (K) is equal to 5.