Comparative analysis of logistic regression and artificial neural network for computer-aided diagnosis of breast masses.
OBJECTIVE
To compare logistic regression and artificial neural network for computer-aided diagnosis on breast sonograms.


METHODS
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.


CONCLUSIONS
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.
Song
Jae H
JH
Department of Electrical Engineering, Pennsylvania Medical Center, 3400 Spruce Street, Philadelphia, PA 19104, USA.
Venkatesh
Santosh S
SS
Conant
Emily A
EA
Arger
Peter H
PH
Sehgal
Chandra M
CM
D001943:Breast Neoplasms / Q000000981:diagnostic imaging*; D003936:Diagnosis, Computer-Assisted / Q000706:statistics & numerical data*; D003937:Diagnosis, Differential; D005260:Female; D006801:Humans; D016571:Neural Networks, Computer; D012372:ROC Curve; D012044:Regression Analysis; D016217:Ultrasonography, Mammary / Q000706:statistics & numerical data*