NDLI: An efficient Computer Aided Decision Support System for breast cancer diagnosis using Echo State Network classifier

Content Provider	IEEE Xplore Digital Library
Author	Wajid, Summrina Kanwal Hussain, Amir Luo, Bin
Copyright Year	2014
Description	Author affiliation: School of Computing Science & Technology, Anhui University, China (Luo, Bin) \|\| Computing Science & Mathematics, School of Natural Sciences, University of Stirling, UK (Wajid, Summrina Kanwal) \|\| Anhui University, China (Hussain, Amir)
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.
Starting Page	17
Ending Page	24
File Size	1004913
Page Count	8
File Format	PDF
ISBN	9781479945276
DOI	10.1109/CICARE.2014.7007829
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-12-09
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machines Echo State Network (ESN) Histograms Accuracy Feature extraction Educational institutions Computer Aided Decision Support Systems (CADSSs) Local Energy based Shape Histogram (LESH) Kernel Cancer
Content Type	Text
Resource Type	Article

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