Comparison Analysis of Linear Discriminant Analysis and Cuckoo-Search Algorithm in the Classification of Breast Cancer from Digital Mammograms

S R, Sannasi Chakravarthy; Rajaguru, Harikumar

doi:10.31557/APJCP.2019.20.8.2333

Comparison Analysis of Linear Discriminant Analysis and Cuckoo-Search Algorithm in the Classification of Breast Cancer from Digital Mammograms

Document Type : Research Articles

Authors

Department of Electronics and Communication Engineering, Anna University (Bannari Amman Institute of Technology), Sathyamangalam, India.

10.31557/APJCP.2019.20.8.2333

Abstract

Objective: Breast cancer is the most common invasive severity which leads to the second primary cause of death
among women. The objective of this paper is to propose a computer-aided approach for the breast cancer classification
from the digital mammograms. Methods: Designing an effective classification approach will assist in resolving the
difficulties in analyzing digital mammograms. The proposed work utilized the Mammogram Image Analysis Society
(MIAS) database for the analysis of breast cancer. Five distinct wavelet families are used for extraction of features
from the mammograms of MIAS database. These extracted features are statistical in nature and served as input to the
Linear Discriminant Analysis (LDA) and Cuckoo-Search Algorithm (CSA) classifiers. Results: Error rate, Sensitivity,
Specificity and Accuracy are the performance measures used and the obtained results clearly state that the CSA used
as a classifier affords an accuracy of 97.5% while compared with the LDA classifier. Conclusion: The results of
comparative performance analysis show that the CSA classifier outperforms the performance of LDA in terms of breast
cancer classification.

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