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Ultrasound Image Discrimination between Benign and Malignant Adnexal Masses Based on a Neural Network Approach.

Publication ,  Journal Article
Aramendía-Vidaurreta, V; Cabeza, R; Villanueva, A; Navallas, J; Alcázar, JL
Published in: Ultrasound in medicine & biology
March 2016

The discrimination between benign and malignant adnexal masses in ultrasound images represents one of the most challenging problems in gynecologic practice. In the study described here, a new method for automatic discrimination of adnexal masses based on a neural networks approach was tested. The proposed method first calculates seven different types of characteristics (local binary pattern, fractal dimension, entropy, invariant moments, gray level co-occurrence matrix, law texture energy and Gabor wavelet) from ultrasound images of the ovary, from which several features are extracted and collected together with the clinical patient age. The proposed technique was validated using 106 benign and 39 malignant images obtained from 145 patients, corresponding to its probability of appearance in general population. On evaluation of the classifier, an accuracy of 98.78%, sensitivity of 98.50%, specificity of 98.90% and area under the curve of 0.997 were calculated.

Published In

Ultrasound in medicine & biology

DOI

EISSN

1879-291X

ISSN

0301-5629

Publication Date

March 2016

Volume

42

Issue

3

Start / End Page

742 / 752

Related Subject Headings

  • Ultrasonography
  • Sensitivity and Specificity
  • Reproducibility of Results
  • Pattern Recognition, Automated
  • Ovarian Neoplasms
  • Neural Networks, Computer
  • Middle Aged
  • Image Interpretation, Computer-Assisted
  • Image Enhancement
  • Humans
 

Citation

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Aramendía-Vidaurreta, V., Cabeza, R., Villanueva, A., Navallas, J., & Alcázar, J. L. (2016). Ultrasound Image Discrimination between Benign and Malignant Adnexal Masses Based on a Neural Network Approach. Ultrasound in Medicine & Biology, 42(3), 742–752. https://doi.org/10.1016/j.ultrasmedbio.2015.11.014
Aramendía-Vidaurreta, Verónica, Rafael Cabeza, Arantxa Villanueva, Javier Navallas, and Juan Luis Alcázar. “Ultrasound Image Discrimination between Benign and Malignant Adnexal Masses Based on a Neural Network Approach.Ultrasound in Medicine & Biology 42, no. 3 (March 2016): 742–52. https://doi.org/10.1016/j.ultrasmedbio.2015.11.014.
Aramendía-Vidaurreta V, Cabeza R, Villanueva A, Navallas J, Alcázar JL. Ultrasound Image Discrimination between Benign and Malignant Adnexal Masses Based on a Neural Network Approach. Ultrasound in medicine & biology. 2016 Mar;42(3):742–52.
Aramendía-Vidaurreta, Verónica, et al. “Ultrasound Image Discrimination between Benign and Malignant Adnexal Masses Based on a Neural Network Approach.Ultrasound in Medicine & Biology, vol. 42, no. 3, Mar. 2016, pp. 742–52. Epmc, doi:10.1016/j.ultrasmedbio.2015.11.014.
Aramendía-Vidaurreta V, Cabeza R, Villanueva A, Navallas J, Alcázar JL. Ultrasound Image Discrimination between Benign and Malignant Adnexal Masses Based on a Neural Network Approach. Ultrasound in medicine & biology. 2016 Mar;42(3):742–752.
Journal cover image

Published In

Ultrasound in medicine & biology

DOI

EISSN

1879-291X

ISSN

0301-5629

Publication Date

March 2016

Volume

42

Issue

3

Start / End Page

742 / 752

Related Subject Headings

  • Ultrasonography
  • Sensitivity and Specificity
  • Reproducibility of Results
  • Pattern Recognition, Automated
  • Ovarian Neoplasms
  • Neural Networks, Computer
  • Middle Aged
  • Image Interpretation, Computer-Assisted
  • Image Enhancement
  • Humans