Comparative analysis of logistic regression and artificial neural network for computer-aided diagnosis of breast masses.
Acad Radiol
; 12(4): 487-95, 2005 Apr.
Article
em En
| MEDLINE
| ID: mdl-15831423
ABSTRACT
RATIONALE AND OBJECTIVE:
To compare logistic regression and artificial neural network for computer-aided diagnosis on breast sonograms. MATERIALS ANDMETHODS:
Ultrasound images of 24 malignant and 30 benign masses were analyzed quantitatively for margin sharpness, margin echogenicity, and angular variation in margin. These features and age of patients were used with two pattern classifiers, logistic regression, and an artificial neural network to differentiate between malignant and benign masses. The performance of two methods was compared by receiver operating characteristic (ROC) analysis.RESULTS:
The area under the ROC curve Az (+/-SD) of the logistic regression analysis was 0.853 +/- 0.059 with 95% confidence limit (0.760-0.950). The area under the ROC curve of the artificial neural network analysis was 0.856 +/- 0.058 with 95% confidence limit (0.734-0.936). Although both the logistic regression and the artificial neural network had the same area under the ROC curve, the shapes of two curves were different. At 95% sensitivity, the artificial neural network had 76.5% specificity, whereas logistic regression had 64.7% specificity.CONCLUSION:
There was no difference in performance between logistic regression and the artificial neural network as measured by the area under the ROC curve. However, at a fixed 95% sensitivity, the artificial neural network had higher (12%) specificity compared with logistic regression value.
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Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Neoplasias da Mama
/
Análise de Regressão
/
Ultrassonografia Mamária
/
Diagnóstico por Computador
/
Redes Neurais de Computação
Tipo de estudo:
Diagnostic_studies
/
Prognostic_studies
Limite:
Female
/
Humans
Idioma:
En
Ano de publicação:
2005
Tipo de documento:
Article