Temat: Klasyfikacja sieci - Katalog OPAC zbiorów

Skocz do pozycji: 1.

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: 2.

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: 3.

Tytuł:: The dedicated decision support system in recognition of some uncertain disease entities
Autorzy:: Porwik, P.
Powiązania:: https://bibliotekanauki.pl/articles/333041.pdf
Data publikacji:: 2009
Wydawca:: Uniwersytet Śląski. Wydział Informatyki i Nauki o Materiałach. Instytut Informatyki. Zakład Systemów Komputerowych
Tematy:: rozpoznawanie obrazu
klasyfikacja danych
sieci neuronowe
systemy wspomagania decyzji
image recognition
data classification
neural network
decision support systems
Opis:: This work presents the principles of image recognition, where quality-based methods are applied. The neural networks and additional software have been proposed. This goal was achieved by using non-parametric recognition algorithms. In this paper the two-state hybrid classification method has been proposed, where artificial intelligence algorithm is included. In recognition process, the learning method, selection and optimization of diagnostic parameters have been introduced. The integrated part of the classifier structure is voting mechanism, which indicates incorrect states of the system – for example the unrecognized images. Effectiveness of the system has been shown by means of examples, where ambiguous data have been incorporated – it is very often a practice of medical diagnostics.
Źródło:: Journal of Medical Informatics & Technologies; 2009, 13; 97-100
1642-6037
Pojawia się w:: Journal of Medical Informatics & Technologies
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 4.

Tytuł:: The diseases classification method on gait abnormalities characteristic contributions
Autorzy:: Chandzlik, S.
Piecha, J.
Powiązania:: https://bibliotekanauki.pl/articles/333759.pdf
Data publikacji:: 2005
Wydawca:: Uniwersytet Śląski. Wydział Informatyki i Nauki o Materiałach. Instytut Informatyki. Zakład Systemów Komputerowych
Tematy:: klasyfikacja chorób neurologicznych
choroba Parkinsona
niedowład
udar niedokrwienny mózgu
automatyczne zakończenie
sieci nuronowe
neurological disease classification
Parkinson disease
hemiparesis
ischemic stroke
automatic conclusion
neural networks
Opis:: Present medicine uses computers in various applications, especially in a field of a diseases level classification and diagnosis. In many cases an automatic conclusion making units are the main goal of the computer systems usage. The software units are developed for the diseases classification or for monitoring of the disease medical treatment. An example application was described in this paper. It concerns a gait abnormalities level analysis that is described by a data records gathered by insoles of Parotec System for Windows (PSW) [17,18]. The PSW software package is used for visualisation of the gait characteristic static and dynamic characteristic features. In the authors' works many additional data components were distinguished. The field of the applications is located within the neurological gait characteristics also the source applications concern orthopaedics [16,18]. Careful analysis of the data provided the developers with new areas the PSW applications [4,11,13]. For conclusion making units the artificial networks theory was implemented [2,4,11,13]. For more effective training of the neural networks specific characteristic measures were introduced [4,5]. They allow controlling the training process more precisely, avoiding mistakes in current records classification.
Źródło:: Journal of Medical Informatics & Technologies; 2005, 9; 187-194
1642-6037
Pojawia się w:: Journal of Medical Informatics & Technologies
Dostawca treści:: Biblioteka Nauki

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