An interval prototype classifier based on a parameterized distance applied to breast thermographic images.
Med Biol Eng Comput
; 55(6): 873-884, 2017 Jun.
Article
em En
| MEDLINE
| ID: mdl-27629552
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.
Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Mama
/
Neoplasias da Mama
Tipo de estudo:
Diagnostic_studies
Limite:
Female
/
Humans
País/Região como assunto:
America do sul
/
Brasil
Idioma:
En
Revista:
Med Biol Eng Comput
Ano de publicação:
2017
Tipo de documento:
Article
País de afiliação:
Brasil
País de publicação:
Estados Unidos