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Pap smear image classification using convolutional neural network
KTH, School of Technology and Health (STH).
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2016 (English)In: ACM International Conference Proceeding Series, Association for Computing Machinery , 2016Conference paper, Published paper (Refereed)
Abstract [en]

This article presents the result of a comprehensive study on deep learning based Computer Aided Diagnostic techniques for classification of cervical dysplasia using Pap smear images. All the experiments are performed on a real indigenous image database containing 1611 images, generated at two diagnostic centres. Focus is given on constructing an effective feature vector which can perform multiple level of representation of the features hidden in a Pap smear image. For this purpose Deep Convolutional Neural Network is used, followed by feature selection using an unsupervised technique with Maximal Information Compression Index as similarity measure. Finally performance of two classifiers namely Least Square Support Vector Machine (LSSVM) and Softmax Regression are monitored and classifier selection is performed based on five measures along with five fold cross validation technique. Output classes reflects the established Bethesda system of classification for identifying pre-cancerous and cancerous lesion of cervix. The proposed system is also compared with two existing conventional systems and also tested on a publicly available database. Experimental results and comparison shows that proposed system performs efficiently in Pap smear classification.

Place, publisher, year, edition, pages
Association for Computing Machinery , 2016.
Keywords [en]
Deep learning, LSSVM, Pap smear image, Softmax regression
National Category
Medical Engineering
Identifiers
URN: urn:nbn:se:kth:diva-207446DOI: 10.1145/3009977.3010068ISI: 000403654700055Scopus ID: 2-s2.0-85014841160ISBN: 9781450347532 (print)OAI: oai:DiVA.org:kth-207446DiVA, id: diva2:1097932
Conference
10th Indian Conference on Computer Vision, Graphics and Image Processing, ICVGIP 2016, 18 December 2016 through 22 December 2016
Note

QC 20170523

Available from: 2017-05-23 Created: 2017-05-23 Last updated: 2017-07-11Bibliographically approved

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CiteExportLink to record
Permanent link

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Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • 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