Building a Stage 1 Computer Aided Detector for Breast Cancer Using Genetic Programming
We describe a fully automated workflow for performing stage 1 breast cancer detection with GP as its cornerstone. Mammograms are by far the most widely used method for detecting breast cancer in women, and its use in national screening can have a dramatic impact on early detection and survival rates. With the increased availability of digital mammography, it is becoming increasingly more feasible to use automated methods to help with detection.
A stage 1 detector examines mammograms and highlights suspicious areas that require further investigation. A too conservative approach degenerates to marking every mammogram (or segment of) as suspicious, while missing a cancerous area can be disastrous.
Our workflow positions us right at the data collection phase such that we generate textural features ourselves. These are fed through our system, which performs PCA on them before passing the most salient ones to GP to generate classifiers. The classifiers give results of 100% accuracy on true positives and a false positive per image rating of just 1.5, which is better than prior work. Not only this, but our system can use GP as part of a feedback loop, to both select and help generate further features.
KeywordsGenetic Programming Classification Mammography
Unable to display preview. Download preview PDF.
- 1.Tabar, L., et al.: A new era in the diagnosis of breast cancer. Surgical Oncology Clinics of North America 9(2), 233–277 (2000)Google Scholar
- 2.Sampat, M., Markey, M., Bovik, A.C.: Computer-aided detection and diagnosis in mammography. In Bovik, A.C., (ed.): Handbook of Image and Video Processing. Elsevier Academic Press (2010)Google Scholar
- 4.American College of Radiology: ACR BIRADS Mammography, Ultrasound & MRI, 4th edn. American College of Radiology, Reston (2003)Google Scholar
- 8.Ahmad, A.M., Khan, G.M., Mahmud, S.A., Miller, J.F.: Breast cancer detection using cartesian genetic programming evolved artificial neural networks. In: Soule, T., et al. (eds.) GECCO 2012: Proceedings of the Fourteenth International Conference on Genetic and Evolutionary Computation Conference, Philadelphia, Pennsylvania, USA, July 7-11, pp. 1031–1038. ACM (2012)Google Scholar
- 11.Nandi, R.J., Nandi, A.K., Rangayyan, R., Scutt, D.: Genetic programming and feature selection for classification of breast masses in mammograms. In: 28th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 2006, New York, USA, pp. 3021–3024. IEEE (August 2006)Google Scholar
- 13.Haralick, R., et al.: Texture features for image classification. IEEE Transactions on Systems, Man, and Cybernetics 3(6) (1973)Google Scholar
- 14.MATLAB: version 8.2 (R2012a). MathWorks Inc., Natick, MA (2013)Google Scholar
- 15.Heath, M., Bowyer, K., Kopans, D., Moore, R., Kegelmeyer, W.P.: The digital database for screening mammography. In: Yaffe, M. (ed.) Proceedings of the Fifth International Workshop on Digital Mammography, pp. 212–218. Medical Physics Publishing (2001)Google Scholar
- 17.Fitzgerald, J., Ryan, C.: A hybrid approach to the problem of class imbalance. In: International Conference on Soft Computing, Brno, Czech Republic (June 2013)Google Scholar
- 18.Fitzgerald, J., Ryan, C.: Exploring boundaries: optimising individual class boundaries for binary classification problem. In: Proceedings of the Fourteenth International Conference on Genetic and Evolutionary Computation Conference, GECCO 2012, pp. 743–750. ACM, New York (2012)CrossRefGoogle Scholar
- 20.Stober, P., Yeh, S.T.: An explicit functional form specification approach to estimate the area under a receiver operating characteristic (roc) curve, vol. 7 (2007), http://www2.sas.com/proceedings/sugi27/p226--227.pdf7
- 22.Whitcher, B., Schmid, V.J., Thornton, A.: Working with the DICOM and NIfTI data standards in R. Journal of Statistical Software 44(6), 1–28 (2011)Google Scholar
- 23.Hu, M.: Visual pattern recognition by moment invariants. Trans. Info. Theory IT-8, 179–187 (1962)Google Scholar
- 24.Ojala, T., Pietikäinen, M., Harwood, D.: Performance evaluation of texture measures with classification based on kullback discrimination of distributions. In: Proceedings of the 12th IAPR International Conference on Pattern Recognition (ICPR 1994), pp. 582–585. IEEE (1994)Google Scholar