Use of genetic algorithms for computer-aided diagnosis of breast cancers from image features

Purpose: In this investigation we have explored 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. Methods: 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. Results: 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 +/- 0.07, somewhat less than those obtained using backpropagation for ANN training: 0.90 +/- 0.05. New or breakthrough work: 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. Conclusions: 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.