Genetic algorithm based detection of breast cancer using least square-support vector machine classifier

K. Gowrishankar; Sudhakara Pandian; G. Narmadha; Muthukumar Vellaisamy; S. Deivasigamani; Manickam Ramasamy

Author(s)

K. Gowrishankar Department of Electrical and Electronics Engineering, AMET University,Chennai, Tamil Nadu,India

Sudhakara Pandian Department of Manufacturing Engineering, VIT University, Vellore Campus,,India

G. Narmadha Department of Electrical and Electronics Engineering, Sethu Institute of Technology,Virudhunagar, Tamil Nadu,India

Muthukumar Vellaisamy Faculty of Computing and Engineering, QUEST International University,Ipoh,Malaysia

S. Deivasigamani Faculty of Engineering, Technology and Built Environment, UCSI University,Kuala Lumpur,Malaysia

Manickam Ramasamy Faculty of Engineering, Technology and Built Environment, UCSI University,Kuala Lumpur,Malaysia

Abstract

Breast tumors are a dangerous disease among women worldwide. They are the second leading cause of death among all forms of cancers in women. Their early detection is critical to increasing the survival rate of women. Mammography is a reliable screening technique in the early detection of abnormal breast tissue severity. Radiologist abnormalities in the breast tissue, radiologists employ mammography. However, detecting breast abnormalities through digital diagnostic techniques by a radiologist could be time consuming. Consequently, computerized studying of digital mammography has emerged via the development of CAD systems. Several CAD systems have been developed for breast cancer detection. However, obtaining a satisfactory performance of CAD systems is a challenging task. We propose a CAD architecture for the classification of breast tissues as either benign or malignant using an LS-SVM classifier with various kernels namely linear, quadratic, polynomial, MLP, and RBF kernels. From the experimental outputs, it is clear that GA based LS-SVM classifier with RBF kernel outputs classification accuracy of 94.59% for normal/abnormal case classification is better, when it is compared with all other kernels. It is also stated that GA based LS-SVM classifier with RBF kernel produces a better classification accuracy of 98.26% for benign/malignant case classification when it is compared with other reported works.

Publication Details

Page(s) 2390-2400

DOI DOI not available

Published Journal: ARPN Journal of Engineering and Applied Sciences, Volume: 18, Issue: 21, Year: 2023

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Keywords

genetic algorithm, mammographic image analysis society, shearlet transform, Breast cancer detection, LS-SVM classifier

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