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2 Bio-Algorithms and Med-Systems

2 2011

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Role of image processing in the cancer diagnosis

Detyna J., Jeleń Ł., Jeleń M.

Bio-Algorithms and Med-Systems

2011

Vol. 7, no. 4

5--9

Cancer is still one of the most deadly diseases. 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 cytological image processing, which is an integral part of a diagnosis process. In this work we present a classification system for grading cancer malignancy. In particular, issues of image processing in the aspect of medical diagnosis presented by prof. R. Tadeusiewicz and Dr. J. Śmietański in [1].

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% .