An improved KPLS-KELM method for breast cancer detection

Abstract

The conception of a Computer-Aided Diagnosis system (CAD) using Artificial Intelligence (AI) is a hot topic in the domain of medical diagnosis. Recently, many approaches have been developed. In the proposed work, a novel classification technique from mammograms based on Kernel Extreme Learning Machine (KELM) and Kernel Partial Least Square (KPLS) method is introduced. The suggested algorithm first used the KPLS algorithm to extract features from the images. The extracted characteristics were then sent to the KELM classifier. In order to improve the generalization of the proposed approach, the cross-validation strategy was used. The simulation results were tested on the Mammographic Image Analysis Society (MIAS) dataset and measured using accuracy, F score, sensitivity, and specificity analysis. These results were compared to existing approaches tested on the same dataset and it was observed that the proposed work is the most efficient.