Abstract
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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Tabar, L., et al.: A new era in the diagnosis of breast cancer. Surgical Oncology Clinics of North America 9(2), 233–277 (2000)
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)
Tot, T., Tabar, L., Dean, P.B.: The pressing need for better histologic-mammographic correlation of the many variations in normal breast anatomy. Virchows Archiv 437(4), 338–344 (2000)
American College of Radiology: ACR BIRADS Mammography, Ultrasound & MRI, 4th edn. American College of Radiology, Reston (2003)
Li, H., et al.: Computerized radiographic mass detection part i: Lesion site selection by morphological enhancement and contextual segmentation. IEEE Trans. Med. Imag. 20, 289–301 (2001)
Polakowski, W.E., Cournoyer, D.A., Rogers, S.K.: Computer-aided breast cancer detection and diagnosis of masses using difference of gaussians and derivative-based feature saliency. IEEE Trans. Med. Imag. 16, 811–819 (1997)
Ganesan, K., et al.: Decision support system for breast cancer detection using mammograms. Proceedings of the Institution of Mechanical Engineers, Part H: Journal of Engineering in Medicine 227(7), 721–732 (2013)
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)
Ludwig, S.A., Roos, S.: Prognosis of breast cancer using genetic programming. In: Setchi, R., Jordanov, I., Howlett, R.J., Jain, L.C. (eds.) KES 2010, Part IV. LNCS, vol. 6279, pp. 536–545. Springer, Heidelberg (2010)
Langdon, W., Harrison, A.: Gp on spmd parallel graphics hardware for mega bioinformatics data mining. Soft Computing 12(12), 1169–1183 (2008)
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)
Völk, K., Miller, J.F., Smith, S.L.: Multiple network CGP for the classification of mammograms. In: Giacobini, M., et al. (eds.) EvoWorkshops 2009. LNCS, vol. 5484, pp. 405–413. Springer, Heidelberg (2009)
Haralick, R., et al.: Texture features for image classification. IEEE Transactions on Systems, Man, and Cybernetics 3(6) (1973)
MATLAB: version 8.2 (R2012a). MathWorks Inc., Natick, MA (2013)
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)
Kerlikowske, K., Grady, D., Barclay, J., Sickles, E.A., Eaton, A., Ernster, V.: Positive predictive value of screening mammography by age and family history of breast cancer. Journal of the American Medical Association 270, 2444–2450 (1993)
Fitzgerald, J., Ryan, C.: A hybrid approach to the problem of class imbalance. In: International Conference on Soft Computing, Brno, Czech Republic (June 2013)
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)
Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: Nsga-ii. IEEE Transactions on Evolutionary Computation 6(2), 182–197 (2002)
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
Geisser, S.: Predictive Inference. Chapman and Hall, New York (1993)
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)
Hu, M.: Visual pattern recognition by moment invariants. Trans. Info. Theory IT-8, 179–187 (1962)
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)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Ryan, C., Krawiec, K., O’Reilly, UM., Fitzgerald, J., Medernach, D. (2014). Building a Stage 1 Computer Aided Detector for Breast Cancer Using Genetic Programming. In: Nicolau, M., et al. Genetic Programming. EuroGP 2014. Lecture Notes in Computer Science, vol 8599. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-44303-3_14
Download citation
DOI: https://doi.org/10.1007/978-3-662-44303-3_14
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-662-44302-6
Online ISBN: 978-3-662-44303-3
eBook Packages: Computer ScienceComputer Science (R0)