Medical image classification via SVM using LBP features from saliency-based folded data

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Good results on image classification and retrieval using support vector machines (SVM) with local binary patterns (LBPs) as features have been extensively reported in the literature where an entire image is retrieved or classified. In contrast, in medical imaging, not all parts of the image may be equally significant or relevant to the image retrieval application at hand. For instance, in lung x-ray image, the lung region may contain a tumour, hence being highly significant whereas the surrounding area does not contain significant information from medical diagnosis perspective. In this paper, we propose to detect salient regions of images during training and fold the data to reduce the effect of irrelevant regions. As a result, smaller image areas will be used for LBP features calculation and consequently classification by SVM. We use IRMA 2009 dataset with 14,410 xray images to verify the performance of the proposed approach. The results demonstrate the benefits of saliency-based folding approach that delivers comparable classification accuracies with state-of-the-art but exhibits lower computational cost and storage requirements, factors highly important for big data analytics.

Original languageEnglish (US)
Title of host publicationProceedings - 2015 IEEE 14th International Conference on Machine Learning and Applications, ICMLA 2015
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages128-132
Number of pages5
ISBN (Electronic)9781509002870
DOIs
StatePublished - Mar 2 2016
EventIEEE 14th International Conference on Machine Learning and Applications, ICMLA 2015 - Miami, United States
Duration: Dec 9 2015Dec 11 2015

Publication series

NameProceedings - 2015 IEEE 14th International Conference on Machine Learning and Applications, ICMLA 2015

Conference

ConferenceIEEE 14th International Conference on Machine Learning and Applications, ICMLA 2015
Country/TerritoryUnited States
CityMiami
Period12/9/1512/11/15

Keywords

  • Folding
  • Image classification
  • Local binary patterns
  • Saliency
  • Support vector machines

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications

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