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2018 | OriginalPaper | Chapter

An Improved Mammogram Classification Approach Using Back Propagation Neural Network

Authors : Aman Gautam, Vikrant Bhateja, Ananya Tiwari, Suresh Chandra Satapathy

Published in: Data Engineering and Intelligent Computing

Publisher: Springer Singapore

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Abstract

Mammograms are generally contaminated by quantum noise, degrading their visual quality and thereby the performance of the classifier in Computer-Aided Diagnosis (CAD). Hence, enhancement of mammograms is necessary to improve the visual quality and detectability of the anomalies present in the breasts. In this paper, a sigmoid based non-linear function has been applied for contrast enhancement of mammograms. The enhanced mammograms are used to define the texture of the detected anomaly using Gray Level Co-occurrence Matrix (GLCM) features. Later, a Back Propagation Artificial Neural Network (BP-ANN) is used as a classification tool for segregating the mammogram into abnormal or normal. The proposed classifier approach has reported to be the one with considerably better accuracy in comparison to other existing approaches.

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previous chapter Bi-Temporal Versioning of Schema in Temporal Data Warehouses
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Metadata
Title
An Improved Mammogram Classification Approach Using Back Propagation Neural Network
Authors
Aman Gautam
Vikrant Bhateja
Ananya Tiwari
Suresh Chandra Satapathy
Copyright Year
2018
Publisher
Springer Singapore
Book
Data Engineering and Intelligent Computing

Print ISBN: 978-981-10-3222-6

Electronic ISBN: 978-981-10-3223-3

Copyright Year: 2018

DOI
https://doi.org/10.1007/978-981-10-3223-3_35

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