By Topic

Application of artificial neural networks for diagnosis of breast cancer

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Lo, J.Y. ; Dept. of Radiol., Duke Univ., Durham, NC, USA ; Floyd, C.E., Jr.

We review four current projects pertaining to artificial neural network (ANN) models that merge radiologist-extracted findings to perform computer aided diagnosis (CADx) of breast cancer. These projects are: (1) prediction of breast lesion malignancy using mammographic findings; (2) classification of malignant lesions as in situ vs. invasive cancer; (3) prediction of breast mass malignancy using ultrasound findings; and (4) the evaluation of CADx models in a cross-institution study. These projects share in common the use of feedforward error backpropagation ANNs. Inputs to the ANNs are medical findings such as mammographic or ultrasound lesion descriptors and patient history data. The output is the biopsy outcome (benign vs. malignant, or in situ vs. invasive cancer) which is being predicted. All ANNs undergo supervised training using actual patient data. These ANN decision models may assist in the management of patients with breast lesions, such as by reducing the number of unnecessary surgical procedures and their associated cost

Published in:

Evolutionary Computation, 1999. CEC 99. Proceedings of the 1999 Congress on  (Volume:3 )

Date of Conference:

1999