An interval prototype classifier based on a parameterized distance applied to breast thermographic images
| Autor: | Renata M. C. R. de Souza, Telmo de Menezes e Silva Filho, Marcus Costa de Araújo, Rita de Cássia Fernandes de Lima |
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| Rok vydání: | 2016 |
| Předmět: |
Engineering
0206 medical engineering Biomedical Engineering Parameterized complexity Breast Neoplasms 02 engineering and technology computer.software_genre Sensitivity and Specificity Symbolic data analysis Interval data Breast cancer 0202 electrical engineering electronic engineering information engineering medicine Humans Breast Mahalanobis distance business.industry Temperature medicine.disease 020601 biomedical engineering Computer Science Applications Thermography Breast thermography Female 020201 artificial intelligence & image processing Data mining business computer Classifier (UML) Algorithms Brazil |
| Zdroj: | Medical & Biological Engineering & Computing. 55:873-884 |
| ISSN: | 1741-0444 0140-0118 |
| DOI: | 10.1007/s11517-016-1565-y |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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