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Normalized Neural Networks for Breast Cancer Classification
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering.
Effat Univ, Saudi Arabia.
2020 (English)In: PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON MEDICAL AND BIOLOGICAL ENGINEERING, CMBEBIH 2019, SPRINGER , 2020, Vol. 73, p. 519-524Conference paper, Published paper (Refereed)
Abstract [en]

In almost all parts of the world, breast cancer is one of the major causes of death among women. But at the same time, it is one of the most curable cancers if it is diagnosed at early stage. This paper tries to find a model that diagnose and classify breast cancer with high accuracy and help to both patients and doctors in the future. Here we develop a model using Normalized Multi Layer Perceptron Neural Network to classify breast cancer with high accuracy. The results achieved is very good (accuracy is 99.27%). It is very promising result compared to previous researches where Artificial Neural Networks were used. As benchmark test, Breast Cancer Wisconsin (Original) was used.

Place, publisher, year, edition, pages
SPRINGER , 2020. Vol. 73, p. 519-524
Series
IFMBE Proceedings, ISSN 1680-0737
Keywords [en]
Breast cancer diagnosis; Supervised learning; Normalized neural networks; Multi layer perceptron; Breast cancer Wisconsin
National Category
Cancer and Oncology
Identifiers
URN: urn:nbn:se:liu:diva-161585DOI: 10.1007/978-3-030-17971-7_77ISI: 000491311000077ISBN: 978-3-030-17971-7 (electronic)ISBN: 978-3-030-17970-0 (print)OAI: oai:DiVA.org:liu-161585DiVA, id: diva2:1368243
Conference
International Conference on Medical and Biological Engineering in Bosnia and Herzegovina (CMBEBIH)
Available from: 2019-11-06 Created: 2019-11-06 Last updated: 2019-11-06

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Alickovic, Emina
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Automatic ControlFaculty of Science & Engineering
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CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • oxford
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf