Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/100274
Full metadata record
DC FieldValueLanguage
dc.contributor.authorAcharya, U. Rajendraen
dc.contributor.authorNg, Eddie Yin-Kweeen
dc.contributor.authorTan, Jen Hongen
dc.contributor.authorSree, Subbhuraam Vinithaen
dc.date.accessioned2013-09-23T08:13:44Zen
dc.date.accessioned2019-12-06T20:19:27Z-
dc.date.available2013-09-23T08:13:44Zen
dc.date.available2019-12-06T20:19:27Z-
dc.date.copyright2010en
dc.date.issued2010en
dc.identifier.citationAcharya, U. R., Ng, E. Y. K., Tan, J.-H., & Sree, S. V. (2010). Thermography based breast cancer detection using texture features and support vector machine. Journal of medical systems, 36(3), 1503-1510.en
dc.identifier.urihttps://hdl.handle.net/10356/100274-
dc.description.abstractBreast cancer is a leading cause of death nowadays in women throughout the world. In developed countries, it is the most common type of cancer in women, and it is the second or third most common malignancy in developing countries. The cancer incidence is gradually increasing and remains a significant public health concern. The limitations of mammography as a screening and diagnostic modality, especially in young women with dense breasts, necessitated the development of novel and more effective strategies with high sensitivity and specificity. Thermal imaging (thermography) is a noninvasive imaging procedure used to record the thermal patterns using Infrared (IR) camera. The aim of this study is to evaluate the feasibility of using thermal imaging as a potential tool for detecting breast cancer. In this work, we have used 50 IR breast images (25 normal and 25 cancerous) collected from Singapore General Hospital, Singapore. Texture features were extracted from co-occurrence matrix and run length matrix. Subsequently, these features were fed to the Support Vector Machine (SVM) classifier for automatic classification of normal and malignant breast conditions. Our proposed system gave an accuracy of 88.10%, sensitivity and specificity of 85.71% and 90.48% respectively.en
dc.language.isoenen
dc.relation.ispartofseriesJournal of medical systemsen
dc.titleThermography based breast cancer detection using texture features and support vector machineen
dc.typeJournal Articleen
dc.contributor.schoolSchool of Mechanical and Aerospace Engineeringen
dc.identifier.doi10.1007/s10916-010-9611-zen
item.fulltextNo Fulltext-
item.grantfulltextnone-
Appears in Collections:MAE Journal Articles

SCOPUSTM   
Citations

179
checked on Sep 6, 2020

WEB OF SCIENCETM
Citations

146
checked on Oct 23, 2020

Page view(s)

388
checked on Oct 24, 2020

Google ScholarTM

Check

Altmetric


Plumx

Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.