Autor: Krawczyk, B - Katalog OPAC zbiorów

Skocz do pozycji: 1.

Tytuł:: Hypertension diagnosis using compound pattern recognition methods
Autorzy:: Krawczyk, B.
Woźniak, M.
Powiązania:: https://bibliotekanauki.pl/articles/333544.pdf
Data publikacji:: 2011
Wydawca:: Uniwersytet Śląski. Wydział Informatyki i Nauki o Materiałach. Instytut Informatyki. Zakład Systemów Komputerowych
Tematy:: rozpoznawanie wzorców
metody złożone
zespoły klasyfikatora
uczenie maszynowe
niesymetryczna klasyfikacja
uznanie dwustopniowe
pattern recognition
compound methods
classifier ensembles
machine learning
unbalanced classification
two-stage recognition
Opis:: The paper presents a hypertension type classification task where the decisions should be made only on the basis of blood pressure, general information and basis biochemical data. This problem has a great importance to the medical decision support systems, yet results achieved so far are not satisfactory. When the canonical approaches tend to fail we should look for the compound pattern recognition systems, such as multiple classifiers systems. This article presents the results of an experimental investigation of the pool of compound classifiers which have their origin in classifiers ensembles, random forest, and random subspace. Presented methods returned good, satisfactory results, outperforming canonical approaches for this problem.
Źródło:: Journal of Medical Informatics & Technologies; 2011, 18; 41-50
1642-6037
Pojawia się w:: Journal of Medical Informatics & Technologies
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 2.

Tytuł:: Automatic detection and counting of platelets in microscopic image
Autorzy:: Burduk, R.
Krawczyk, B.
Powiązania:: https://bibliotekanauki.pl/articles/333065.pdf
Data publikacji:: 2010
Wydawca:: Uniwersytet Śląski. Wydział Informatyki i Nauki o Materiałach. Instytut Informatyki. Zakład Systemów Komputerowych
Tematy:: rozpoznawanie wzorców
nauczanie maszynowe
analiza obrazu
pattern recognition
bioinformatic
machine learning
image analysis
platelet
Opis:: In this paper we present a machine learning-based approach for detecting platelet cells in microscopic smear images. Counting how many platelets appeared in each smear image is one of the basic tasks done in many laboratories. In many cases this is still done by a human — laboratory technician. Due to very small size and often great quantity of those cells, precise estimating of the number of platelets is not a trivial task. As in all man-dependent problems the whole process is very sensitive to errors, time-consuming and its accuracy is limited by human perception. We propose alternative, fully automatic solution that is free of those drawbacks. Our idea is based on the combination of techniques driven from two fields of modern computer science: the image analysis and pattern recognition ⁄ machine learning. It not only reduces the error rate, but, what is more important, also decreases the time needed for each smear image analysis. The obtained results are very satisfying and our solution is more precise than estimation based on human perception. This will improve the quality of laboratory work and allow to save time that can be spent on other important tasks.
Źródło:: Journal of Medical Informatics & Technologies; 2010, 16; 173-178
1642-6037
Pojawia się w:: Journal of Medical Informatics & Technologies
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 3.

Tytuł:: Handling class label noise in medical pattern classification systems
Autorzy:: Sáez, J. A.
Krawczyk, B.
Woźniak, M.
Powiązania:: https://bibliotekanauki.pl/articles/333813.pdf
Data publikacji:: 2015
Wydawca:: Uniwersytet Śląski. Wydział Informatyki i Nauki o Materiałach. Instytut Informatyki. Zakład Systemów Komputerowych
Tematy:: machine learning
pattern classification
class noise
noise filtering
decision support systems
uczenie maszynowe
klasyfikacja wzorców
filtracja zakłóceń
filtracja szumów
systemy wspomagania decyzji
Opis:: Pattern classification systems play an important role in medical decision support. They allow to automatize and speed-up the data analysis process, while being able to handle complex and massive amounts of information and discover new knowledge. However, their quality is based on the classification models built, which require a training set. In supervised classification we must supply class labels to each training sample, which is usually done by domain experts or some automatic systems. As both of these approaches cannot be deemed as flawless, there is a chance that the dataset is corrupted by class noise. In such a situation, class labels are wrongly assigned to objects, which may negatively affect the classifier training process and impair the classification performance. In this contribution, we analyze the usefulness of existing tools to deal with class noise, known as noise filtering methods, in the context of medical pattern classification. The experiments carried out on several real-world medical datasets prove the importance of noise filtering as a pre-processing step and its beneficial influence on the obtained classification accuracy.
Źródło:: Journal of Medical Informatics & Technologies; 2015, 24; 123-130
1642-6037
Pojawia się w:: Journal of Medical Informatics & Technologies
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 4.

Tytuł:: Classification techniques for non-invasive recognition of liver fibrosis stage
Autorzy:: Krawczyk, B.
Woźniak, M.
Orczyk, T.
Porwik, P.
Musialik, J.
Błońska-Fajfrowska, B.
Powiązania:: https://bibliotekanauki.pl/articles/332969.pdf
Data publikacji:: 2012
Wydawca:: Uniwersytet Śląski. Wydział Informatyki i Nauki o Materiałach. Instytut Informatyki. Zakład Systemów Komputerowych
Tematy:: uczenie maszynowe
systemy wielo-klasyfikatorów
informatyka medyczna
zwłóknienie wątroby
wirusowe zapalenie wątroby typu C
machine learning
multiple classifier systems
compound pattern recognition
medical informatics
liver fibrosis
hepatitis C
Opis:: Contemporary medicine should provide high quality diagnostic services while at the same time remaining as comfortable as possible for a patient. Therefore novel non-invasive disease recognition methods are becoming one of the key issues in the health services domain. Analysis of data from such examinations opens an interdisciplinary bridge between the medical research and artificial intelligence. The paper presents application of machine learning techniques to biomedical data coming from indirect examination method of the liver fibrosis stage. Presented approach is based on a common set of non-invasive blood test results. The performance of four different compound machine learning algorithms, namely Bagging, Boosting, Random Forest and Random Subspaces, is examined and grid search method is used to find the best setting of their parameters. Extensive experimental investigations, carried out on a dataset collected by authors, show that automatic methods achieve a satisfactory level of the fibrosis level recognition and may be used as a real-time medical decision support system for this task.
Źródło:: Journal of Medical Informatics & Technologies; 2012, 20; 121-127
1642-6037
Pojawia się w:: Journal of Medical Informatics & Technologies
Dostawca treści:: Biblioteka Nauki

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