MGWO-CNN: hyperparameter optimization of CNN classifier for cervical cancer detection using Modified Grey Wolf Optimizer

Every nation reports an increasing number of deaths among women due to cervical cancer each day, which has become a major global issue. Rapid diagnosis and treatment of cervical cancer can reduce mortality rates. Most studies on cervical cancer detection have made use of ensemble methods and Convolutional Neural Network (CNN) models. However, overfitting, parameter adjustment, and gradient vanishing issues affect most of these models. To address these issues, we propose a Modified Grey Wolf Optimizer-based Convolutional Neural Network (MGWO-CNN) model that incorporates the concepts of chaos theory and differential evolution mutation to detect cervical cancer. Traditionally, neural networks combined with backpropagation achieve poor convergence due to their dependence on initial values. Metaheuristic techniques offer a superior alternative to backpropagation. The proposed technique adjusts CNN hyperparameters to train the model architecture effectively. This optimized model extracts key features from cervical Pap smear images and predicts the outcomes. The results demonstrate that the MGWO-CNN model is a remarkably effective method to detect cervical cancer. We evaluated the efficiency of the model by comparing its performance on two datasets (Herlev and SIPaKMeD) using four performance measures: accuracy, sensitivity, specificity, and precision. The proposed approach outperforms existing methods in terms of accuracy, precision, sensitivity and specificity, achieving values of 99.45%, 100%, 97.96%, and 100%, respectively.