NDLI: An interval prototype classifier based on a parameterized distance applied to breast thermographic images

Content Provider	Springer Nature Link
Author	Araújo, Marcus C. Souza, Renata M. C. R. Lima, Rita C. F. Filho, Telmo M. Silva
Copyright Year	2016
Abstract	Breast cancer is one of the leading causes of death in women. Because of this, thermographic images have received a refocus for diagnosing this cancer type. This work proposes an innovative approach to classify breast abnormalities (malignant, benignant and cyst), employing interval temperature data in order to detect breast cancer. The learning step takes into account the internal variation of the intervals when describing breast abnormalities and uses a way to map these intervals into a space where they can be more easily separated. The method builds class prototypes, and the allocation step is based on a parameterized Mahalanobis distance for interval-valued data. The proposed classifier is applied to a breast thermography dataset from Brazil with 50 patients. We investigate two different scenarios for parameter configuration. The first scenario focuses on the overall misclassification rate and achieves 16 % misclassification rate and 93 % sensitivity to the malignant class. The second scenario maximizes the sensitivity to the malignant class, achieving 100 % sensitivity to this specific class, along with 20 % overall misclassification rate. We compare the performances of our approach and of many methods taken from the literature of interval data classification for the breast thermography task. Results show that our method outperforms competing algorithms.
Starting Page	873
Ending Page	884
Page Count	12
File Format	PDF
ISSN	01400118
Journal	Medical and Biological Engineering and Computing
Volume Number	55
Issue Number	6
e-ISSN	17410444
Language	English
Publisher	Springer Berlin Heidelberg
Publisher Date	2016-09-15
Publisher Place	Berlin, Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Thermography Symbolic data analysis Interval data Classification Breast cancer Human Physiology Biomedical Engineering Imaging Radiology Computer Applications
Content Type	Text
Resource Type	Article
Subject	Biomedical Engineering Computer Science Applications

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