Temat: klasyfikacja - Katalog OPAC zbiorów

Skocz do pozycji: 1.

Tytuł:: Clustering algorithm for classification methods
Autorzy:: Łęski, J.
Jeżewski, M.
Powiązania:: https://bibliotekanauki.pl/articles/333004.pdf
Data publikacji:: 2012
Wydawca:: Uniwersytet Śląski. Wydział Informatyki i Nauki o Materiałach. Instytut Informatyki. Zakład Systemów Komputerowych
Tematy:: grupowanie
klasyfikacja
clustering
classification
class boundaries
Opis:: Classification plays an important role in many fields of life, including medical diagnosis support. In the paper, fuzzy clustering algorithm dedicated to classification methods is proposed. Its goal is to find pairs of prototypes located near boundaries of both classes of objects. The minimization procedure of the proposed criterion function is described. The algorithm for determining the value of the clustering parameter is also presented. Presented results (synthetic dataset) confirm correctness of clustering - most of final prototypes, determined based on obtained pairs, are located between boundary of two classes.
Źródło:: Journal of Medical Informatics & Technologies; 2012, 20; 11-18
1642-6037
Pojawia się w:: Journal of Medical Informatics & Technologies
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 2.

Tytuł:: An approach to unsupervised classification
Autorzy:: Przybyła, T.
Pander, T.
Horoba, K.
Kupka, T.
Matonia, A.
Powiązania:: https://bibliotekanauki.pl/articles/333363.pdf
Data publikacji:: 2011
Wydawca:: Uniwersytet Śląski. Wydział Informatyki i Nauki o Materiałach. Instytut Informatyki. Zakład Systemów Komputerowych
Tematy:: klasyfikacja
grupowanie rozmyte
klasyfikacja nienadzorowana
klasyfikator najbliższych sąsiadów
classification
fuzzy clustering
unsupervised classification
nearest neighbour classifier
Opis:: Classification methods can be divided into supervised and unsupervised methods. The supervised classifier requires a training set for the classifier parameter estimation. In the case of absence of a training set, the popular classifiers (e.g. K-Nearest Neighbors) can not be used. The clustering methods are considered as unsupervised classification methods. This paper presents an idea of the unsupervised classification with the popular classifiers. The fuzzy clustering method is used to create a learning set. The learning set includes only these patterns that are the best representative of each class in the input dataset. The numerical experiment uses an artificial dataset as well as the medical datasets (PIMA, Wisconsin Breast Cancer) and illustrates the usefulness of the proposed method.
Źródło:: Journal of Medical Informatics & Technologies; 2011, 17; 105-111
1642-6037
Pojawia się w:: Journal of Medical Informatics & Technologies
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 3.

Tytuł:: Human activity detection based on the iBeacon technology
Autorzy:: Lewandowski, M.
Orczyk, T.
Płaczek, B.
Powiązania:: https://bibliotekanauki.pl/articles/333122.pdf
Data publikacji:: 2016
Wydawca:: Uniwersytet Śląski. Wydział Informatyki i Nauki o Materiałach. Instytut Informatyki. Zakład Systemów Komputerowych
Tematy:: classification
embedded systems
iBeacon
klasyfikacja
systemy wbudowane
Opis:: Paper presents a new method of patient activity monitoring, by using modern ADL (Activities of Daily Living) techniques. Proposed method utilizes energy efficient Bluetooth iBeacon BLE (Bluetooth Low Energy) modules, developed by Apple. Main advantage of this technology is the ability to detect neighboring devices, which belong to the same device family. Proposed method is based on observing changes of received signal strength indicator (RSSI) in the time domain. The RSSI analysis is performed in order to asses a human activity. Such observation may be particularly useful for monitoring consciousness of elder people, where reaction time of emergency rescuers and appropriate rescue operations may save the human lives.
Źródło:: Journal of Medical Informatics & Technologies; 2016, 25; 38-45
1642-6037
Pojawia się w:: Journal of Medical Informatics & Technologies
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 4.

Tytuł:: The influence of cardiotocogram signal feature selection method on fetal state assessment efficacy
Autorzy:: Jeżewski, M.
Czabański, R.
Łęski, J.
Powiązania:: https://bibliotekanauki.pl/articles/333440.pdf
Data publikacji:: 2014
Wydawca:: Uniwersytet Śląski. Wydział Informatyki i Nauki o Materiałach. Instytut Informatyki. Zakład Systemów Komputerowych
Tematy:: cardiotocography
classification
feature selection
kardiotokografia
klasyfikacja
selekcja cech
Opis:: Cardiotocographic (CTG) monitoring is a method of assessing fetal state. Since visual analysis of CTG signal is difficult, methods of automated qualitative fetal state evaluation on the basis of the quantitative description of the signal are applied. The appropriate selection of learning data influences the quality of the fetal state assessment with computational intelligence methods. In the presented work we examined three different feature selection procedures based on: principal components analysis, receiver operating characteristics and guidelines of International Federation of Gynecology and Obstetrics. To investigate their influence on the fetal state assessment quality the benchmark SisPorto® dataset and the Lagrangian support vector machine were used.
Źródło:: Journal of Medical Informatics & Technologies; 2014, 23; 51-58
1642-6037
Pojawia się w:: Journal of Medical Informatics & Technologies
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 5.

Tytuł:: Application of modified fuzzy clustering to medical data classification
Autorzy:: Jeżewski, M.
Powiązania:: https://bibliotekanauki.pl/articles/333509.pdf
Data publikacji:: 2011
Wydawca:: Uniwersytet Śląski. Wydział Informatyki i Nauki o Materiałach. Instytut Informatyki. Zakład Systemów Komputerowych
Tematy:: grupowanie rozmyte
klasyfikacja
dane medyczne
fuzzy clustering
classification
medical data
Opis:: Classification plays very important role in medical diagnosis. This paper presents fuzzy clustering method dedicated to classification algorithms. It focuses on two additional sub-methods modifying obtained clustering prototypes and leading to final prototypes, which are used for creating the classifier fuzzy if-then rules. The main goal of that work was to examine a performance of the classifier which uses such rules. Commonly used including medical benchmark databases were applied. In order to validate the results, each database was represented by 100 pairs of learning and testing subsets. The obtained classification quality was better in relation to the one of the best classifiers - Lagrangian SVM and suggests that presented clustering with additional sub-methods are appropriate to application to classification algorithms.
Źródło:: Journal of Medical Informatics & Technologies; 2011, 17; 51-57
1642-6037
Pojawia się w:: Journal of Medical Informatics & Technologies
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 6.

Tytuł:: A computer aided dignostic system for survival analysis after EVAR treatment of EVAR
Autorzy:: Maiora, J.
Grańa, M.
Powiązania:: https://bibliotekanauki.pl/articles/333534.pdf
Data publikacji:: 2011
Wydawca:: Uniwersytet Śląski. Wydział Informatyki i Nauki o Materiałach. Instytut Informatyki. Zakład Systemów Komputerowych
Tematy:: analiza obrazów medycznych
rejestracja
klasyfikacja
medical image analysis
registration
classification
Opis:: Abdominal Aortic Aneurysm (AAA) is a local dilation of the Aorta that occurs between the renal and iliac arteries. Recently developed treatment involves the insertion of a endovascular prosthetic (EVAR), which has the advantage of being a minimally invasive procedure but also requires monitoring to analyze postoperative patient outcomes. The most widespread method for monitoring is computerized axial tomography (CAT) imaging, which allows 3D reconstructions and segmentations of the aorta's lumen of the patient under study. Previously published methods measure the deformation of the aorta between two studies of the same patient using image registration techniques. This paper applies neural network and statistical classifiers to build a predictor of patient survival. The features used for classification are the volume registration quality measures after each of the image registration steps. This system provides the medical team an additional decision support tool.
Źródło:: Journal of Medical Informatics & Technologies; 2011, 18; 51-58
1642-6037
Pojawia się w:: Journal of Medical Informatics & Technologies
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 7.

Tytuł:: Personal identity verification method based on lips photographs
Autorzy:: Wrobel, K.
Doroz, R.
Porwik, P.
Naruniec, J.
Kowalski, M.
Powiązania:: https://bibliotekanauki.pl/articles/333548.pdf
Data publikacji:: 2015
Wydawca:: Uniwersytet Śląski. Wydział Informatyki i Nauki o Materiałach. Instytut Informatyki. Zakład Systemów Komputerowych
Tematy:: biometrics
lip features
data classification
biometria
cechy wargi
klasyfikacja danych
Opis:: The paper presents a personal identification method based on lips photographs. This method uses a new approach to the extraction and classification of characteristic features of the mouth from photographs. It eliminates the drawbacks that occur during the acquisition of lip print images with the use of the forensic method that requires special tools. Geometrical dimensions of the entire mouth as well as of the upper and lower lips were adopted as the features, on the basis of which the verification is performed. An ensemble classifier was used for the classification of the features obtained. The effectiveness of the classifier has been verified experimentally.
Źródło:: Journal of Medical Informatics & Technologies; 2015, 24; 59-65
1642-6037
Pojawia się w:: Journal of Medical Informatics & Technologies
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 8.

Tytuł:: Feature selection for breast cancer malignancy classification problem
Autorzy:: Filipczuk, P.
Kowal, M.
Marciniak, A.
Powiązania:: https://bibliotekanauki.pl/articles/333614.pdf
Data publikacji:: 2010
Wydawca:: Uniwersytet Śląski. Wydział Informatyki i Nauki o Materiałach. Instytut Informatyki. Zakład Systemów Komputerowych
Tematy:: wybór funkcji
klasyfikacja
rak piersi
feature selection
classification
breast cancer
Opis:: The paper provides a preview of some work in progress on the computer system to support breast cancer diagnosis. Diagnosis approach is based on microscope images of the FNB (Fine Needle Biopsy) and assumes distinguishing malignant from benign cases. Studies conducted focus on two different problems, the first concern the extraction of morphometric parameters of nuclei present in cytological images and the other concentrate on breast cancer nature classification using selected features. Studies in both areas are conducted in parallel. This work is devoted to the problem of feature selection from the set of determined features in order to maximize the accuracy of classification. Morphometric features are derived directly from a digital scans of breast fine needle biopsy slides and are computed for segmented nuclei. The quality of feature space is measured with four different classification methods. In order to illustrate the effectiveness of the approach, the automatic system of malignancy classification was applied on a set of medical images with promising results.
Źródło:: Journal of Medical Informatics & Technologies; 2010, 15; 193-199
1642-6037
Pojawia się w:: Journal of Medical Informatics & Technologies
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 9.

Tytuł:: Computer-aided diagnosis of breast cancer using gaussian mixture cytological image segmentation
Autorzy:: Kowal, M.
Filipczuk, P.
Obuchowicz, A.
Korbicz, J.
Powiązania:: https://bibliotekanauki.pl/articles/333385.pdf
Data publikacji:: 2011
Wydawca:: Uniwersytet Śląski. Wydział Informatyki i Nauki o Materiałach. Instytut Informatyki. Zakład Systemów Komputerowych
Tematy:: segmentacja obrazu
klasyfikacja
rak piersi
image segmentation
classification
breast cancer
Opis:: This paper presents an automatic computer system to breast cancer diagnosis. System was designed to distinguish benign from malignant tumors based on fine needle biopsy microscope images. Studies conducted focus on two different problems, the first concern the extraction of morphometric and colorimetric parameters of nuclei from cytological images and the other concentrate on breast cancer classification. In order to extract the nuclei features, segmentation procedure that integrates results of adaptive thresholding and Gaussian mixture clustering was implemented. Next, tumors were classified using four different classification methods: k–nearest neighbors, naive Bayes, decision trees and classifiers ensemble. Diagnostic accuracy obtained for conducted experiments varies according to different classification methods and fluctuates up to 98% for quasi optimal subset of features. All computational experiments were carried out using microscope images collected from 25 benign and 25 malignant lesions cases.
Źródło:: Journal of Medical Informatics & Technologies; 2011, 17; 257-262
1642-6037
Pojawia się w:: Journal of Medical Informatics & Technologies
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 10.

Tytuł:: Classification of breast thermal images using artificial neural networks
Autorzy:: Jakubowska, T.
Wiecek, B.
Wysocki, M.
Drews-Peszynski, C.
Strzelecki, M.
Powiązania:: https://bibliotekanauki.pl/articles/333564.pdf
Data publikacji:: 2004
Wydawca:: Uniwersytet Śląski. Wydział Informatyki i Nauki o Materiałach. Instytut Informatyki. Zakład Systemów Komputerowych
Tematy:: przetwarzanie termogramów
sieci nuronowe
klasyfikacja
thermal image processing
neural network
classification
Opis:: In this paper we present classification of the thermal images in order to discriminate healthy and pathological cases during breast cancer screening. Different image features and approaches for data reduction and classification have been used to distinguish healthy breast one with malignant tumour. We use image histogram and co-occurrence matrix to get thermal signatures and analyze symmetry between left and right side. The most promised method was based on wavelet transformation and nonlinear neural network classifier. The proposed approach was used in the pilot investigations in the medical centre which is permanently using thermograph for breast cancer screening, as an adjacent method for other classical diagnostic method, such as mammography.
Źródło:: Journal of Medical Informatics & Technologies; 2004, 7; MIP41-50
1642-6037
Pojawia się w:: Journal of Medical Informatics & Technologies
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 11.

Tytuł:: Medical diagnosis using fuzzy cognitive map classifier
Autorzy:: Froelich, W.
Wrobel, K.
Powiązania:: https://bibliotekanauki.pl/articles/333970.pdf
Data publikacji:: 2015
Wydawca:: Uniwersytet Śląski. Wydział Informatyki i Nauki o Materiałach. Instytut Informatyki. Zakład Systemów Komputerowych
Tematy:: fuzzy cognitive map
medical diagnosis
classification
rozmyta mapa poznawcza
diagnostyka medyczna
klasyfikacja
Opis:: In this study, we address the problem of medical diagnosis by applying Fuzzy Cognitive Map (FCM). A distinctive feature of the FCM is its ability to simulate the development of the disease in time. By this simulation, it is possible to predict the severity of the disease by having future knowledge on current medical investigations. For the first time in this paper, we construct an FCM-based classifier dedicated solely to perform medical diagnosis. To learn the FCM, we use an evolutionary algorithm explicitly specifying the newly designed fitness function. Real, publicly available medical data are applied for the validation and evaluation of the proposed approach.
Źródło:: Journal of Medical Informatics & Technologies; 2015, 24; 247-254
1642-6037
Pojawia się w:: Journal of Medical Informatics & Technologies
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 12.

Tytuł:: Diagnosing Parkinson’s disease using the classification of speech signals
Autorzy:: Froelich, W.
Wróbel, K.
Porwik, P.
Powiązania:: https://bibliotekanauki.pl/articles/333984.pdf
Data publikacji:: 2014
Wydawca:: Uniwersytet Śląski. Wydział Informatyki i Nauki o Materiałach. Instytut Informatyki. Zakład Systemów Komputerowych
Tematy:: Parkinson's disease
medical diagnosis
data classification
choroba Parkinsona
diagnostyka medyczna
klasyfikacja danych
Opis:: This paper addressees the problem of an early diagnosis of Parkinson’s disease by the classification of characteristic features of person’s voice. A new, two-step classification approach is proposed. In the first step, the voice samples are classified using standard state-of-the-art classifiers. In the second step, the classified samples are assigned to patients and the final classification process based on majority criterion is performed. The advantage of using our new approach is the resulting, reliable patientoriented medical diagnose. The proposed two-step method of classification allows also to deal with the variable number of voice samples gathered for every patient. Preliminary experiments revealed quite satisfactory classification accuracy obtained during the performed leave-one-out cross validation.
Źródło:: Journal of Medical Informatics & Technologies; 2014, 23; 187-193
1642-6037
Pojawia się w:: Journal of Medical Informatics & Technologies
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 13.

Tytuł:: Influence of gestational age on neural networks interpretation of fetal monitoring signals
Autorzy:: Jeżewski, M.
Czabański, R.
Horoba, K.
Wróbel, J.
Łęski, J.
Jeżewski, J.
Powiązania:: https://bibliotekanauki.pl/articles/333505.pdf
Data publikacji:: 2008
Wydawca:: Uniwersytet Śląski. Wydział Informatyki i Nauki o Materiałach. Instytut Informatyki. Zakład Systemów Komputerowych
Tematy:: monitoring płodu
kardiotokografia
klasyfikacja
sieci neuronowe
fetal monitoring
cardiotocography
classification
neural networks (NN)
Opis:: Cardiotocographic monitoring (CTG) is a primary biophysical monitoring method for assessment of the fetal state and is based on analysis of fetal heart rate, uterine contraction activity and fetal movement signals. Visual analysis of CTG traces is very difficult so computer-aided fetal monitoring systems have become a standard in clinical centres. We proposed the application of neural networks for the prediction of fetal outcome using the parameters of quantitative description of acquired signals as inputs. We focused on the influence of the gestational age (during trace recording) on the fetal outcome classification quality. We designed MLP and RBF neural networks with changing the number of neurons in the hidden layer to find the best structure. Networks were trained and tested fifty times, with random cases assignment to training, validating and testing subset. We obtained the value of sensitivity index above 0.7, what may be regarded as good result. However additional trace grouping within similar gestational age, increased classification quality in the case of MLP networks.
Źródło:: Journal of Medical Informatics & Technologies; 2008, 12; 137-142
1642-6037
Pojawia się w:: Journal of Medical Informatics & Technologies
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 14.

Tytuł:: An improved medical diagnosing of acute abdominal pain with decision tree
Autorzy:: Jankowski, D.
Jackowski, K.
Powiązania:: https://bibliotekanauki.pl/articles/333015.pdf
Data publikacji:: 2012
Wydawca:: Uniwersytet Śląski. Wydział Informatyki i Nauki o Materiałach. Instytut Informatyki. Zakład Systemów Komputerowych
Tematy:: medyczne systemy wspomagania decyzji
drzewa decyzyjne
klasyfikacja
medical decision support systems
decision trees
classification
Opis:: In medical decision making (e.g., classification) we expect that decision will be made effectively and reliably. Decision making systems with their ability to learn automatically seem to be very appropriate for performing such tasks. Decision trees provide high classification accuracy with simple representation of gathered knowledge. Those advantages cause that decision trees have been widely used in different areas of medical decision making. In this paper we present characteristic of univariate and multivariate decision tree. We apply those classifiers to the problem of acute abdominal pain diagnosis.
Źródło:: Journal of Medical Informatics & Technologies; 2012, 20; 65-71
1642-6037
Pojawia się w:: Journal of Medical Informatics & Technologies
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 15.

Tytuł:: Evaluation of predictive capabilities of quantitative cardiotocographic signal features
Autorzy:: Czabański, R.
Jeżewski, M.
Roj, D.
Szaszkowski, Z.
Kupka, T.
Wróbel, J.
Powiązania:: https://bibliotekanauki.pl/articles/332937.pdf
Data publikacji:: 2010
Wydawca:: Uniwersytet Śląski. Wydział Informatyki i Nauki o Materiałach. Instytut Informatyki. Zakład Systemów Komputerowych
Tematy:: monitorowanie płodu
tętno płodu
klasyfikacja sygnału
fetal monitoring
fetal heart rate
signal classification
ROC analysis
Opis:: Cardiotocography (CTG) is the main method of assessment of the fetal state during pregnancy and labour used in clinical practice. It is based on quantitative analysis of fetal heart rate, fetal movements and uterine contractions signals. The evaluation of the CTG signals can be made using criteria recommended by International Federation of Obstetrics and Gynecology. Nevertheless, the diagnosis verification is possible only after the delivery on the basis of newborn assessment. In the proposed work we evaluated the capacity of quantitative analysis of CTG traces in predicting fetal outcome. The relationship between CTG signal features and attributes of fetal outcome was assessed on the basis of ROC curves analysis. The obtained results indicate the adequate predictive capabilities of the selected CTG features especially for fetal outcome assessed with Apgar score and suggest the necessity of applying the criteria for the CTG traces evaluation that are related to the gestational age. Our study also shows the value of the CTG monitoring as a screening procedure providing appropriate confirmation of fetal wellbeing.
Źródło:: Journal of Medical Informatics & Technologies; 2010, 16; 11-17
1642-6037
Pojawia się w:: Journal of Medical Informatics & Technologies
Dostawca treści:: Biblioteka Nauki

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