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Erschienen in:
Rank-Revealing Orthogonal Decomposition in Extreme Learning Machine Design Kapitel lesen Erstes Kapitel lesen

2018 | OriginalPaper | Buchkapitel

An Improved CAD Framework for Digital Mammogram Classification Using Compound Local Binary Pattern and Chaotic Whale Optimization-Based Kernel Extreme Learning Machine

verfasst von : Figlu Mohanty, Suvendu Rup, Bodhisattva Dash

Erschienen in: Artificial Neural Networks and Machine Learning – ICANN 2018

Verlag: Springer International Publishing

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Abstract

The morbidity and mortality rate of breast cancer still continues to remain high among women across the world. This figure can be reduced if the cancer is identified at its early stage. A Computer-aided diagnosis (CAD) system is an efficient computerized tool used to analyze the mammograms for finding cancer in the breast and to reach a decision with maximum accuracy. The presented work aims at developing a CAD model which can classify the mammograms as normal or abnormal, and further, benign or malignant accurately. In the present model, CLAHE is used for image pre-processing, compound local binary pattern (CM-LBP) for feature extraction followed by principal component analysis (PCA) for feature reduction. Then, a chaotic whale optimization-based kernel extreme learning machine (CWO-KELM) is utilized to classify the mammograms as normal/abnormal and benign/malignant. The present model achieves the highest accuracy of 100% and 99.48% for MIAS and DDSM, respectively.

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Vorheriges Kapitel Rank-Revealing Orthogonal Decomposition in Extreme Learning Machine Design
Nächstes Kapitel A Novel Echo State Network Model Using Bayesian Ridge Regression and Independent Component Analysis
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Metadaten
Titel
An Improved CAD Framework for Digital Mammogram Classification Using Compound Local Binary Pattern and Chaotic Whale Optimization-Based Kernel Extreme Learning Machine
verfasst von
Figlu Mohanty
Suvendu Rup
Bodhisattva Dash
Copyright-Jahr
2018
Buch
Artificial Neural Networks and Machine Learning – ICANN 2018

Print ISBN: 978-3-030-01420-9

Electronic ISBN: 978-3-030-01421-6

Copyright-Jahr: 2018

DOI
https://doi.org/10.1007/978-3-030-01421-6_2