mdh.sePublications
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • 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
A Cascade Classifier for Diagnosis of Melanoma in Clinical Images
Auckland University of Technology, New Zealand.
Auckland University of Technology, New Zealand.
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.ORCID iD: 0000-0002-1550-0994
Auckland University of Technology, New Zealand.
2014 (English)In: The 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society IEEE EMBC14, 2014, p. 6748-6751Conference paper, Published paper (Refereed)
Abstract [en]

Computer aided diagnosis of medical images can help physicians in better detecting and early diagnosis of many symptoms and therefore reducing the mortality rate. Realization of an efficient mobile device for semi-automatic diagnosis of melanoma would greatly enhance the applicability of medical image classification scheme and make it useful in clinical contexts. In this paper, interactive object recognition methodology is adopted for border segmentation of clinical skin lesion images. In addition, performance of five classifiers, KNN, Naïve Bayes, multi-layer perceptron, random forest and SVM are compared based on color and texture features for discriminating melanoma from benign nevus. The results show that a sensitivity of 82.6% and specificity of 83% can be achieved using a single SVM classifier. However, a better classification performance was achieved using a proposed cascade classifier with the sensitivity of 83.06% and specificity of 90.05% when performing ten-fold cross validation.

Place, publisher, year, edition, pages
2014. p. 6748-6751
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:mdh:diva-26781DOI: 10.1109/EMBC.2014.6945177Scopus ID: 2-s2.0-84929494210ISBN: 978-1-4244-7929-0 (print)OAI: oai:DiVA.org:mdh-26781DiVA, id: diva2:768558
Conference
The 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society IEEE EMBC14, 26 Aug 2014, Chicago, United States
Projects
RALF3 - Software for Embedded High Performance Architectures
Available from: 2014-12-04 Created: 2014-12-02 Last updated: 2018-02-22Bibliographically approved

Open Access in DiVA

No full text in DiVA

Other links

Publisher's full textScopus

Authority records BETA

Larsson, Thomas B

Search in DiVA

By author/editor
Larsson, Thomas B
By organisation
Embedded Systems
Electrical Engineering, Electronic Engineering, Information Engineering

Search outside of DiVA

GoogleGoogle Scholar

doi
isbn
urn-nbn

Altmetric score

doi
isbn
urn-nbn
Total: 16 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • 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