Pixel N-grams for mammographic lesion classification

Kulkarni, Pradnya, Stranieri, Andrew, Ugon, Julien, Mittal, Manish and Kulkarni, Siddhivinayak 2017, Pixel N-grams for mammographic lesion classification, in CSCITA 2017 2nd International Conference on Communication Systems, Computing and IT Applications, CSCITA 2017 - Proceedings, IEEE, Piscataway, N.J., pp. 107-111, doi: 10.1109/CSCITA.2017.8066534.

Attached Files
Name Description MIMEType Size Downloads

Title Pixel N-grams for mammographic lesion classification
Author(s) Kulkarni, Pradnya
Stranieri, Andrew
Ugon, JulienORCID iD for Ugon, Julien orcid.org/0000-0001-5290-8051
Mittal, Manish
Kulkarni, Siddhivinayak
Conference name Communication Systems, Computing and IT Applications. Conference (2017 : 2nd : Mumbai, India)
Conference location Mumbai, India
Conference dates 2017/04/07 - 2017/04/08
Title of proceedings CSCITA 2017 2nd International Conference on Communication Systems, Computing and IT Applications, CSCITA 2017 - Proceedings
Publication date 2017
Start page 107
End page 111
Total pages 5
Publisher IEEE
Place of publication Piscataway, N.J.
Keyword(s) Breast Cancer
Classification
Mammogram
N-gram
Pixel
Summary Automated classification algorithms have been applied to breast cancer diagnosis in order to improve the diagnostic accuracy and turnover time. However, classification accuracy, sensitivity and specificity could still be improved further. Moreover, reducing computational cost is another challenge as the number of images to be analyzed is typically large. In this paper, a novel Pixel N-gram approach inspired from character N-grams in the text retrieval context has been applied for mammographic lesion classification. The experiments on real world database demonstrate that the Pixel N-grams outperform the existing histogram as well as Haralick features with respect to classification accuracy as well as sensitivity. Effect of varying N and using various classifiers is also analyzed in this paper. Results show that optimum value of N is equal to 3 and MLP classifier performs better than SVM and KNN classifier using 3-gram features.
ISBN 9781509043811
Language eng
DOI 10.1109/CSCITA.2017.8066534
HERDC Research category E1.1 Full written paper - refereed
ERA Research output type E Conference publication
Copyright notice ©2017, IEEE
Persistent URL http://hdl.handle.net/10536/DRO/DU:30114232

Document type: Conference Paper
Collection: School of Information Technology
Connect to link resolver
 
Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved.

Versions
Version Filter Type
Citation counts: TR Web of Science Citation Count  Cited 0 times in TR Web of Science
Scopus Citation Count Cited 0 times in Scopus
Google Scholar Search Google Scholar
Access Statistics: 22 Abstract Views, 2 File Downloads  -  Detailed Statistics
Created: Thu, 11 Oct 2018, 14:25:06 EST

Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.