The Optimized Classification of Mammograms Based on the Antlion Technique

Abstract

Breast cancer is one of the main health issues for women. This disease can be cured only if detected at early stages. Digital mammography is used to detect the malignant cells at an early stage. This article designs a methodology to detect the malignant tumors. The methodology is comprised of preprocessing feature extraction by Gabor and Law's feature extraction, and feature reduction by ant-lion optimization as well as a classification step using a SVM classifier which is implemented on the live dataset prepared through the Rajindra Hospital Patiala along with MIAS and DDSM datasets. The results of proposed techniques have been compared with three states of art techniques SVM based classification without feature reduction, PSOWNN i.e. PSO based reduction with a neural network as a classifier and binary gray wolf-based feature reduction with SVM classifier. The performance analysis proves the significance of the technique.