Optimizing Cervical Cancer Classification with SVM and Improved Genetic Algorithm on Pap Smear Images

Abstract

This study presents an approach to optimize cervical cancer classification using Support Vector Machines (SVM) and an improved Genetic Algorithm (GA) on Pap smear images. The proposed methodology involves preprocessing the images, extracting relevant features, and employing a genetic algorithm for feature selection. An SVM classifier is trained using the selected features and optimized using the genetic algorithm. The performance of the optimized model is evaluated, demonstrating improved accuracy and efficiency in cervical cancer classification. The findings hold the potential for assisting healthcare professionals in early cervical cancer diagnosis based on Pap smear images.