Comparative Study on Various Techniques Involved in Designing a Computer Aided Diagnosis (CAD) System for Mammogram Classification

A. R. Mrunalini, A. R. NareshKumar and J. Premaladha
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A. R. Mrunalini: School of Computing, SASTRA Deemed-to-be-University
A. R. NareshKumar: School of Computing, SASTRA Deemed-to-be-University
J. Premaladha: School of Computing, SASTRA Deemed-to-be-University

A chapter in New Trends in Computational Vision and Bio-inspired Computing, 2020, pp 627-639 from Springer

Abstract: Abstract Breast cancer detection can be done using mammography which uses low-dose X-rays to obtain images of the breast in order to detect the abnormalities present in it. Analysing the mammogram can be done by using a Computer Aided Diagnosis (CAD) system to detect abnormalities. Designing a CAD system involves pre-processing, segmentation, feature learning and finally classifying the types of abnormalities using an efficient classifier. This paper compares the various steps and techniques used to design a CAD system and the results obtained are analysed based on their performance. By comparing various methods for pre-processing, segmentation and feature learning, it was found that different methods work well in different contexts. Some of the drawbacks in a few systems are also discussed and future works for further improvements have been suggested.

Keywords: Mammogram; Pre-processing; Segmentation; Feature-Extraction; Classification (search for similar items in EconPapers)
Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-030-41862-5_62

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DOI: 10.1007/978-3-030-41862-5_62

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