Neural Networks (Computer)
"Neural Networks (Computer)" is a descriptor in the National Library of Medicine's controlled vocabulary thesaurus,
MeSH (Medical Subject Headings). Descriptors are arranged in a hierarchical structure,
which enables searching at various levels of specificity.
A computer architecture, implementable in either hardware or software, modeled after biological neural networks. Like the biological system in which the processing capability is a result of the interconnection strengths between arrays of nonlinear processing nodes, computerized neural networks, often called perceptrons or multilayer connectionist models, consist of neuron-like units. A homogeneous group of units makes up a layer. These networks are good at pattern recognition. They are adaptive, performing tasks by example, and thus are better for decision-making than are linear learning machines or cluster analysis. They do not require explicit programming.
|Neural Networks (Computer)
- Neural Networks (Computer)
- Network, Neural (Computer)
- Networks, Neural (Computer)
- Neural Network (Computer)
- Neural Network Models
- Connectionist Models
- Connectionist Model
- Model, Connectionist
- Models, Connectionist
- Models, Neural Network
- Model, Neural Network
- Network Model, Neural
- Network Models, Neural
- Neural Network Model
Below are MeSH descriptors whose meaning is more general than "Neural Networks (Computer)".
Below are MeSH descriptors whose meaning is more specific than "Neural Networks (Computer)".
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Below are the most recent publications written about "Neural Networks (Computer)" by people in Profiles.
Lakhani P, Sundaram B. Deep Learning at Chest Radiography: Automated Classification of Pulmonary Tuberculosis by Using Convolutional Neural Networks. Radiology. 2017 Aug; 284(2):574-582.
Banner MJ, Tams CG, Euliano NR, Stephan PJ, Leavitt TJ, Martin AD, Al-Rawas N, Gabrielli A. Real time noninvasive estimation of work of breathing using facemask leak-corrected tidal volume during noninvasive pressure support: validation study. J Clin Monit Comput. 2016 Jun; 30(3):285-94.
Giordano A, Giuliano M, De Laurentiis M, Eleuteri A, Iorio F, Tagliaferri R, Hortobagyi GN, Pusztai L, De Placido S, Hess K, Cristofanilli M, Reuben JM. Artificial neural network analysis of circulating tumor cells in metastatic breast cancer patients. Breast Cancer Res Treat. 2011 Sep; 129(2):451-8.
Acton PD, Newberg A. Artificial neural network classifier for the diagnosis of Parkinson's disease using [99mTc]TRODAT-1 and SPECT. Phys Med Biol. 2006 Jun 21; 51(12):3057-66.
Crawford AG, Fuhr JP, Clarke J, Hubbs B. Comparative effectiveness of total population versus disease-specific neural network models in predicting medical costs. Dis Manag. 2005 Oct; 8(5):277-87.
Parekattil SJ, Gill IS, Castle EP, Burgess SV, Walls MM, Thomas R, Kumar U, Purifoy JA, Ng CS, Kang Y, Fuchs GJ, Weise ES, Winfield HN, Lallas C, Andrews PE. Multi-institutional validation study of neural networks to predict duration of stay after laparoscopic radical/simple or partial nephrectomy. J Urol. 2005 Oct; 174(4 Pt 1):1380-4.
Jackson-Lewis V, Smeyne RJ. MPTP and SNpc DA neuronal vulnerability: role of dopamine, superoxide and nitric oxide in neurotoxicity. Minireview. Neurotox Res. 2005; 7(3):193-202.
Danion F, Schöner G, Latash ML, Li S, Scholz JP, Zatsiorsky VM. A mode hypothesis for finger interaction during multi-finger force-production tasks. Biol Cybern. 2003 Feb; 88(2):91-8.
Wang L, Buchanan TS. Prediction of joint moments using a neural network model of muscle activations from EMG signals. IEEE Trans Neural Syst Rehabil Eng. 2002 Mar; 10(1):30-7.
Sztandera LM, Goodenday LS, Cios KJ. A neuro-fuzzy algorithm for diagnosis of coronary artery stenosis. Comput Biol Med. 1996 Mar; 26(2):97-111.