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Web–based framework for breast cancer classification

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EN
Abstrakty
EN
The aim of this work is to create a web-based system that will assist its users in the cancer diagnosis process by means of automatic classification of cytological images obtained during fine needle aspiration biopsy. This paper contains a description of the study on the quality of the various algorithms used for the segmentation and classification of breast cancer malignancy. The object of the study is to classify the degree of malignancy of breast cancer cases from fine needle aspiration biopsy images into one of the two classes of malignancy, high or intermediate. For that purpose we have compared 3 segmentation methods: k-means, fuzzy c-means and watershed, and based on these segmentations we have constructed a 25–element feature vector. The feature vector was introduced as an input to 8 classifiers and their accuracy was checked. The results show that the highest classification accuracy of 89.02 % was recorded for the multilayer perceptron. Fuzzy c–means proved to be the most accurate segmentation algorithm, but at the same time it is the most computationally intensive among the three studied segmentation methods.
Twórcy
  • Institute of Computer Engineering, Control and Robotics, Wrocław University of Technology, Wybrze˙ze Wyspia´nskiego 27, 50370 Wrocław, Poland
autor
  • Department of Computer Science and Software Engineering, Concordia University, 1455 De Maisonneuve Blvd. West, Montreal, Quebec, Canada H3G 1M8
autor
  • Department of Computer Science and Software Engineering, Concordia University, 1455 De Maisonneuve Blvd. West, Montreal, Quebec, Canada H3G 1M8
autor
  • Department of Computer Science and Software Engineering, Concordia University, 1455 De Maisonneuve Blvd. West, Montreal, Quebec, Canada H3G 1M8
Bibliografia
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  • [3] TNM breast cancer staging, December 2014.
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  • [11] P. Filipczuk, T. Fevens, A. Krzyzak, and R. Monczak. Computer-aided breast cancer diagnosis based on the analysis of cytological images of fine needle biopsies. IEEE Transactions on Medical Imaging, PP(99):1–1, 2013.
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  • [21] B. Krawczyk, Ł. Jele´n, A. Krzy˙zak, and T. Fevens.Oversampling methods for classification of imbalanced breast cancer malignancy data. Lecture Notes in Computer Science (LNCS), 7594:483–490, 2012.
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Bibliografia
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