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Wyszukujesz frazę "Krzyżak, A.T." wg kryterium: Autor


Wyświetlanie 1-4 z 4
Tytuł:
Classification of breast cancer malignancy using cytological images of fine needle aspiration biopsies
Autorzy:
Jeleń, Ł.
Fevens, T.
Krzyżak, A.
Powiązania:
https://bibliotekanauki.pl/articles/908052.pdf
Data publikacji:
2008
Wydawca:
Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:
złośliwość guza
klasyfikacja guza
rak sutka
skala Blooma-Richardsona
automated malignancy grading
FNA grading
SVM
breast cancer grading
Bloom-Richardson
Opis:
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.
Źródło:
International Journal of Applied Mathematics and Computer Science; 2008, 18, 1; 75-83
1641-876X
2083-8492
Pojawia się w:
International Journal of Applied Mathematics and Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Application of pattern recognition techniques for the analysis of thin blood smear images
Autorzy:
Habibzadeh, M.
Krzyżak, A.
Fevens, T.
Powiązania:
https://bibliotekanauki.pl/articles/333546.pdf
Data publikacji:
2011
Wydawca:
Uniwersytet Śląski. Wydział Informatyki i Nauki o Materiałach. Instytut Informatyki. Zakład Systemów Komputerowych
Tematy:
binaryzacja
segmentacja
granulometria
CBC
microscopic medical denoising
binarization
segmentation
edge preservation
granulometry
Opis:
In this paper we discuss applications of pattern recognition and image processing to automatic processing and analysis of histopathological images. We focus on counting of Red and White blood cells using microscopic images of blood smear samples. We provide literature survey and point out new challenges. We present an improved cell counting algorithm.
Źródło:
Journal of Medical Informatics & Technologies; 2011, 18; 29-40
1642-6037
Pojawia się w:
Journal of Medical Informatics & Technologies
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
GLCM and GLRLM based texture features for computer-aided breast cancer diagnosis
Autorzy:
Filipczuk, P.
Fevens, T.
Krzyżak, A.
Obuchowicz, A.
Powiązania:
https://bibliotekanauki.pl/articles/333264.pdf
Data publikacji:
2012
Wydawca:
Uniwersytet Śląski. Wydział Informatyki i Nauki o Materiałach. Instytut Informatyki. Zakład Systemów Komputerowych
Tematy:
diagnostyka wspomagana komputerowo
analiza teksturalna
rak piersi
computer-aided diagnosis
texture features
breast cancer
Opis:
This paper presents 15 texture features based on GLCM (Gray-Level Co-occurrence Matrix) and GLRLM (Gray-Level Run-Length Matrix) to be used in an automatic computer system for breast cancer diagnosis. The task of the system is to distinguish benign from malignant tumors based on analysis of fine needle biopsy microscopic images. The features were tested whether they provide important diagnostic information. For this purpose the authors used a set of 550 real case medical images obtained from 50 patients of the Regional Hospital in Zielona Góra. The nuclei were isolated from other objects in the images using a hybrid segmentation method based on adaptive thresholding and kmeans clustering. Described texture features were then extracted and used in the classification procedure. Classification was performed using KNN classifier. Obtained results reaching 90% show that presented features are important and may significantly improve computer-aided breast cancer detection based on FNB images.
Źródło:
Journal of Medical Informatics & Technologies; 2012, 19; 109-115
1642-6037
Pojawia się w:
Journal of Medical Informatics & Technologies
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Web–based framework for breast cancer classification
Autorzy:
Bruździński, T.
Krzyżak, A.
Fevens, T.
Jeleń, Ł.
Powiązania:
https://bibliotekanauki.pl/articles/91866.pdf
Data publikacji:
2014
Wydawca:
Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
Tematy:
breast cancer
classification
cytological image
aspiration biopsy
feature vector
classifier
multilayer perceptron
segmentation algorithm
Opis:
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.
Źródło:
Journal of Artificial Intelligence and Soft Computing Research; 2014, 4, 2; 149-162
2083-2567
2449-6499
Pojawia się w:
Journal of Artificial Intelligence and Soft Computing Research
Dostawca treści:
Biblioteka Nauki
Artykuł
    Wyświetlanie 1-4 z 4

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