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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.

Methods of microarray data preprocessing for classification of tumors

Rubel T., Pawłowski Z., Zaremba K.

Biocybernetics and Biomedical Engineering

2005

Vol. 25, no. 4

37-57

Gene arrays measure expression levels for thousands of genes simultaneously, providing a powerful tool for both basic research and clinical medicine. The aim of this paper was to present an optimal approach to preprocessing data from cancer microarray studies. The performance of different statistical methods used for the tumor classification was also compared. These methods include: the Bayes classifier, Fisher's classifier, minimum Euclidean and Mahalanobis distance classifiers and K-nearest neighbours classifier. The preprocessing algorithms and classification methods were applied to three datasets used for diagnosis of lymphoma, leukemia and lung cancer.