Cervical Cancer Detection Using Deep Neural Network and Hybrid Waterwheel Plant Optimization Algorithm

Abstract

More than 85% of the world's cervical cancer fatalities occur in less-developed nations, causing early mortality among women. In this paper, we propose a novel approach for the early classification of cervical cancer based on a new feature selection algorithm and classification method. The new feature selection algorithm is based on a hybrid of the Waterwheel Plant Algorithm and Particle Swarm Optimization algorithms, and bWWPAPSO denotes it. Meanwhile, the new classification method is based on optimizing the parameters of a multilayer perceptron neural network (WWPAPSO+MLP). A publicly available dataset is employed to verify the effectiveness of the proposed approach. Due to this dataset's imbalance and missing values, it is preprocessed and balanced using SMOTETomek, where undersampling and oversampling were utilized. The usefulness of class imbalance and feature selection based on the classifier's specificity, sensitivity, and accuracy has been demonstrated by way of a comparative study of the proposed methodology that has been carried out. WWPAPSO+MLP achieves superior performance, with an accuracy of 97.3% and a sensitivity of 98.8%. On the other hand, several statistical tests were conducted, including the Wilcoxon signed rank test and analysis of variance (ANOVA) to confirm the effectiveness and superiority of the proposed approach.