- Title
- Pixel N-grams for mammographic lesion classification
- Creator
- Kulkarni, Pradnya; Stranieri, Andrew; Ugon, Julien; Mittal, Manish; Kulkarni, Siddhivinayak
- Date
- 2017
- Type
- Text; Conference proceedings
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/180780
- Identifier
- vital:15823
- Identifier
-
https://doi.org/10.1109/CSCITA.2017.8066534
- Identifier
- ISBN:9781509043811
- Abstract
- 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.
- Publisher
- IEEE
- Relation
- 2017 2nd International Conference on Communication Systems, Computing and IT Applications, CSCITA , Mumbai; 7th-8th April, 2017; published in CSCITA 2017 - Proceedings p. 107-111
- Rights
- All metadata describing materials held in, or linked to, the repository is freely available under a CC0 licence
- Rights
- Copyright IEEE
- Subject
- Breast Cancer; Classification; Mammogram; N-gram; Pixel N-gram
- Reviewed
- Funder
- 2017
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