Autor:

Jeleń, Łukasz ; Fevens, Thomas ; Krzyżak, Adam

Współtwórca:

Kummert, Anton - ed. ; Rafajłowicz, Ewaryst - ed.

Tytuł:

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

Podtytuł:

.

Tytuł publikacji grupowej:

AMCS, volume 18 (2008)

Temat i słowa kluczowe:

automated malignancy grading ; FNA grading ; SVM ; breast cancer grading ; Bloom-Richardson

Abstract:

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.

Wydawca:

Zielona Góra: Uniwersytet Zielonogórski

Data wydania:

2008

Typ zasobu:

artykuł

DOI:

10.2478/v10006-008-0007-x

Strony:

75-83

Źródło:

AMCS, volume 18, number 1 (2008) ; kliknij tutaj, żeby przejść

Jezyk:

eng

Licencja CC BY 4.0:

kliknij tutaj, żeby przejść

Prawa do dysponowania publikacją:

Biblioteka Uniwersytetu Zielonogórskiego