Image classification with genetic programming: Building a stage 1 computer aided detector for breast cancer

Abstract

This chapter describes a general approach for image classification using Genetic Programming (GP) and demonstrates this approach through the application of GP to the task of stage 1 cancer detection in digital mammograms. We detail an automated work-flow that begins with image processing and culminates in the evolution of classification models which identify suspicious segments of mammograms. Early detection of breast cancer is directly correlated with survival of the disease and mammography has been shown to be an effective tool for early detection, which is why many countries have introduced national screening programs. However, this presents challenges, as such programs involve screening a large number of women and thus require more trained radiologists at a time when there is a shortage of these professionals in many countries.Also, as mammograms are difficult to read and radiologists typically only have a few minutes allocated to each image, screening programs tend to be conservative-involving many callbacks which increase both the workload of the radiologists and the stress and worry of patients.Fortunately, the relatively recent increase in the availability of mammograms in digital form means that it is now much more feasible to develop automated systems for analysing mammograms. Such systems, if successful could provide a very valuable second reader function.We present a work-flow that begins by processing digital mammograms to segment them into smaller sub-images and to extract features which describe textural aspects of the breast. The most salient of these features are then used in a GP system which generates classifiers capable of identifying which particular segments may have suspicious areas requiring further investigation. An important objective of this work is to evolve classifiers which detect as many cancers as possible but which are not overly conservative. The classifiers give results of 100?% sensitivity and a false positive per image rating of just 0.33, 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.