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Proceedings Paper

Use of genetic algorithms for computer-aided diagnosis of breast cancers from image features
Author(s): Carey E. Floyd; Georgia D. Tourassi; Jay A. Baker
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Paper Abstract

In this investigation we explore genetic algorithms as a technique to train the weights in a feed forward neural network designed to predict breast cancer based on mammographic findings and patient history. Mammograms were obtained from 206 patients who obtained breast biopsies. Mammographic findings were recorded by radiologists for each patient. In addition, the outcome of the biopsy was recorded. Of the 206 cases, 73 were malignant while 133 were benign at the time of biopsy. A genetic algorithm (GA) was developed to adjust the weights of an artificial neural network (ANN) so that the ANN would output the outcome of the biopsy when the mammographic findings were given as inputs. The GA is a technique for function optimization that reflects biological genetic evolution. The ANN was a fully connected feed- forward network using a sigmoid activation with 11 inputs, one hidden layer with 10 nodes, and one output node (benign/malignant). The GA approach allows much flexibility in selecting the function to be optimized. In this work both mean-squared error (MSE) and receiver operating characteristic (ROC) curve area (Az) were explored as optimization criteria. The system was trained using a bootstrap sampling. Optimizing for the two criteria result in different solutions. The 'best' solution was obtained by minimizing a linear combination of MSE and (1-Az). ROC areas were 0.82 plus or minus 0.07, somewhat less than those obtained using backpropagation for ANN training: 0.90 plus or minus 0.05. This is the first description of a genetic algorithm for breast cancer diagnosis. The novel advantage of this technique is the ability to optimize the system for maximizing ROC area rather than minimizing mean squared error. A new technique for computer-aided diagnosis of breast cancer has been explored. The flexibility of the GA approach allows optimization of cost functions that have relevance to breast cancer prediction.

Paper Details

Date Published: 16 April 1996
PDF: 8 pages
Proc. SPIE 2710, Medical Imaging 1996: Image Processing, (16 April 1996); doi: 10.1117/12.237973
Show Author Affiliations
Carey E. Floyd, Duke Univ. Medical Ctr. and Duke Univ. (United States)
Georgia D. Tourassi, Duke Univ. Medical Ctr. (United States)
Jay A. Baker, Duke Univ. Medical Ctr. (United States)


Published in SPIE Proceedings Vol. 2710:
Medical Imaging 1996: Image Processing
Murray H. Loew; Kenneth M. Hanson, Editor(s)

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