An efficient computer aided decision support system for breast cancer diagnosis using Echo State Network classifier

Wajid, Summrina Kanwal; Hussain, Amir; Luo, Bin

Abstract

The paper presents Echo State Network (ESN) as classifier to diagnose the abnormalities in mammogram images. Abnormalities in mammograms can be of different types. An efficient system which can handle these abnormalities and draw correct diagnosis is vital. We experimented with wavelet and Local Energy based Shape Histogram (LESH) features combined with Echo State Network classifier. The suggested system produces high classification accuracy of 98% as well as high sensitivity and specificity rates. We compared the performance of ESN with Support Vector Machine (SVM) and other classifiers and results generated indicate that ESN can compete with benchmark classifier and in some cases beat them. The high rate of Sensitivity and Specificity also signifies the power of ESN classifier to detect positive and negative case correctly.

• Presentation Conference Type: Conference Paper (Published)
• Conference Name: 2014 IEEE Symposium on Computational Intelligence in Healthcare and e-health (CICARE)
• Start Date: Dec 9, 2014
• End Date: Dec 12, 2014
• Online Publication Date: Jan 15, 2015
• Publication Date: 2015
• Deposit Date: Oct 10, 2019
• Publisher: Institute of Electrical and Electronics Engineers
• Pages: 17-24
• Book Title: 2014 IEEE Symposium on Computational Intelligence in Healthcare and e-health (CICARE)
• DOI: https://doi.org/10.1109/CICARE.2014.7007829
• Keywords: Echo State Network (ESN), Computer Aided Decision Support Systems (CADSSs), Local Energy based Shape Histogram (LESH)
• Public URL: http://researchrepository.napier.ac.uk/Output/1792843