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Grading breast cancer malignancy with neural networks

Jeleń Ł., Lipiński A., Detyna J., Jeleń M.

Bio-Algorithms and Med-Systems

2011

Vol. 7, no. 2

47--53

Breast cancer is one of the most often diagnosed cancers among middle-aged women. It is a well known fact that the early diagnosis is crucial and allows for the successful treatment while cancers diagnosed in their late stage are almost impossible to treat. For precise and objective diagnosis there is a need for a computerized method for malignancy grading, which is an integral part of a diagnosis process. In this work we present a classification system for grading cancer malignancy based on the Bloom – Richardson grading scheme. This is a well known grading scheme among the pathologist used during the diagnosis process. To achieve such a classification we extracted 16 features that were then used to classify the malignancy into two classes. Each class represents the malignancy of the cancer according to Bloom – Richardson grading scheme. According to that scheme two types of features are considered, where each type is extracted from images recorded at two different magnifications. Three structural features were calculated from low magnification images and thirteen polymorphic features were derived from high magnification images. To classify the malignancy grades, the multilayer perceptron was used. The described system was able to classify the malignancy with the error rate of 13.5%. In this paper we also present first clinical trials that allow for the verification of the obtained classification rate. The clinical trial showed that the depicted system has a high performance achieving an accuracy of 93.08% .

Classification of breast cancer malignancy using cytological images of fine needle aspiration biopsies

Jeleń Ł., Fevens T., Krzyżak A.

International Journal of Applied Mathematics and Computer Science

2008

Vol. 18, no 1

75-83

According to the World Health Organization (WHO), breast cancer (BC) is one of the most deadly cancers diagnosed among middle-aged women. Precise diagnosis and prognosis are crucial to reduce the high death rate. In this paper we present a framework for automatic malignancy grading of fine needle aspiration biopsy tissue. The malignancy grade is one of the most important factors taken into consideration during the prediction of cancer behavior after the treatment. Our framework is based on a classification using Support Vector Machines (SVM). The SVMs presented here are able to assign a malignancy grade based on preextracted features with the accuracy up to 94.24%. We also show that SVMs performed best out of four tested classifiers.