Using evolutionary computation to learn about detecting breast cancer

Disagreement or inconsistencies in mammographic interpretation motivates utilizing computerized pattern recognition algorithms to aid the assessment of radiographic features. This paper provides a review of recent efforts to evolve neural networks and linear classifiers to assist in the detection of breast cancer. Attention has been given to 216 cases (mammogram series) that presented suspicious characteristics. The domain expert (Wasson) quantified up to 12 radiographic features for each case based on guidelines from previous literature. Patient age was also included. The truth of malignancy or a benign case was available by examining the records of open surgical biopsy (111 malignant, 105 benign). Results indicate that both neural and linear models can yield suitable pattern classifiers and that fundamental relationships between input features and classification can be recognized.

[1]  C. E. Kahn Decision aids in radiology. , 1996, Radiologic clinics of North America.

[2]  David B. Fogel,et al.  Evolutionary Computation: Towards a New Philosophy of Machine Intelligence , 1995 .

[3]  C. Floyd,et al.  Breast cancer: prediction with artificial neural network based on BI-RADS standardized lexicon. , 1995, Radiology.

[4]  C. Floyd,et al.  Prediction of breast cancer malignancy using an artificial neural network , 1994, Cancer.

[5]  F. Girosi,et al.  Networks for approximation and learning , 1990, Proc. IEEE.

[6]  David B. Fogel,et al.  Evolving artificial neural networks for screening features from mammograms , 1998, Artif. Intell. Medicine.

[7]  E C Wasson,et al.  A step toward computer-assisted mammography using evolutionary programming and neural networks. , 1997, Cancer letters.

[8]  M Giger,et al.  Image processing and computer-aided diagnosis. , 1996, Radiologic clinics of North America.

[9]  David B. Fogel,et al.  Linear and neural models for classifying breast masses , 1998, IEEE Transactions on Medical Imaging.

[10]  Peter R. Innocent,et al.  Determining and classifying the region of interest in ultrasonic images of the breast using neural networks , 1996, Artif. Intell. Medicine.

[11]  Thomas Bäck,et al.  Evolutionary computation: comments on the history and current state , 1997, IEEE Trans. Evol. Comput..

[12]  Berkman Sahiner,et al.  Classification of mass and normal breast tissue: a convolution neural network classifier with spatial domain and texture images , 1996, IEEE Trans. Medical Imaging.

[13]  Kurt Hornik,et al.  Multilayer feedforward networks are universal approximators , 1989, Neural Networks.

[14]  B S Monsees Assessment of the recent consensus development panel on screening women aged 40-49 for breast cancer. , 1997, AJR. American journal of roentgenology.

[15]  D B Fogel,et al.  Evolving neural networks for detecting breast cancer. , 1995, Cancer letters.

[16]  T. Tong,et al.  Cancer statistics, 1993 , 1993, CA: a cancer journal for clinicians.

[17]  Thomas Bäck,et al.  Evolutionary computation: Toward a new philosophy of machine intelligence , 1997, Complex..