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Wyświetlanie 1-2 z 2
Tytuł:
Profiling bell’s palsy based on House - Brackmann score
Autorzy:
Song, I.
Vong, J.
Yen, N. Y..
Diederich, J.
Yellowlees, P.
Powiązania:
https://bibliotekanauki.pl/articles/91551.pdf
Data publikacji:
2013
Wydawca:
Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
Tematy:
facial nerve
palsy
support vector machines
SVMs
Emergent Self-Organizing Map
ESOM
House-Brackmann score
facial paralysis
facial image
Opis:
In this study, we propose to diagnose facial nerve palsy using Support Vector Machines (SVMs) and Emergent Self-Organizing Map (ESOM). This research seeks to analyze facial palsy domain using facial features and grade the degree of nerve damage based on the House-Brackmann score. Traditional diagnostic approaches involve a medical doctor recording a thorough history of a patient and determining the onset of paralysis, rate of progression and so on. The most important step is to assess the degree of voluntary movement of the facial nerves and document the grade of facial paralysis using House- Brackmann score. The significance of the work is the attempt to understand the diagnosis and grading processes using semi-supervised learning with the aim of automating the process. The value of the research is in identifying and documenting the limited literature seen in this area. The use of automated diagnosis and grading greatly reduces the duration of medical examination and increases the consistency, because many palsy images are stored to provide benchmark references for comparative purposes. The proposed automated diagnosis and grading are computationally efficient. This automated process makes it ideal for remote diagnosis and examination of facial palsy. The profiling of a large number of facial images are captured using mobile phones and digital cameras.
Źródło:
Journal of Artificial Intelligence and Soft Computing Research; 2013, 3, 1; 41-50
2083-2567
2449-6499
Pojawia się w:
Journal of Artificial Intelligence and Soft Computing Research
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Pulse shape discrimination of neutrons and gamma rays using kohonen artificial neural networks
Autorzy:
Tambouratzis, T.
Chernikova, D.
Pzsit, I.
Powiązania:
https://bibliotekanauki.pl/articles/91759.pdf
Data publikacji:
2013
Wydawca:
Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
Tematy:
shape
neutron
discrimination
gamma rays
Kohonen artificial neural networks
ANNs
linear vector quantisation
LVQ
self-organizing map
SOM
pulse shape discrimination
PSD
Opis:
The potential of two Kohonen artificial neural networks (ANNs) - linear vector quantisation (LVQ) and the self organising map (SOM) - is explored for pulse shape discrimination (PSD), i.e. for distinguishing between neutrons (n’s) and gamma rays (’s). The effect that (a) the energy level, and (b) the relative size of the training and test sets, have on identification accuracy is also evaluated on the given PSD dataset. The two Kohonen ANNs demonstrate complementary discrimination ability on the training and test sets: while the LVQ is consistently more accurate on classifying the training set, the SOM exhibits higher n/ identification rates when classifying new patterns regardless of the proportion of training and test set patterns at the different energy levels; the average time for decision making equals ˜100 μs in the case of the LVQ and ˜450 μs in the case of the SOM.
Źródło:
Journal of Artificial Intelligence and Soft Computing Research; 2013, 3, 2; 77-88
2083-2567
2449-6499
Pojawia się w:
Journal of Artificial Intelligence and Soft Computing Research
Dostawca treści:
Biblioteka Nauki
Artykuł
    Wyświetlanie 1-2 z 2

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