scholarly journals Exploring the Application of Artificial Neural Network in Rural Streamflow Prediction - A Feasibility Study

2018 ◽  
Author(s):  
T. A. Choudhury ◽  
Jackie Wei ◽  
Andrew Barton ◽  
Harpreet Kandra ◽  
Abdul Aziz
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Feasibility Study ◽  
Streamflow Prediction ◽  
Artificial Neural

Oxygen extraction fraction mapping at 3 Tesla using an artificial neural network: A feasibility study

2017 ◽  
Vol 79 (2) ◽  
pp. 890-899 ◽  
Author(s):  
Sebastian Domsch ◽  
Bettina Mürle ◽  
Sebastian Weingärtner ◽  
Jascha Zapp ◽  
Frederik Wenz ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Feasibility Study ◽  
3 Tesla ◽  
Oxygen Extraction Fraction ◽  
Oxygen Extraction ◽  
Extraction Fraction ◽  
Artificial Neural

scholarly journals Artificial Neural Network-Derived Cerebral Metabolic Rate of Oxygen for Differentiating Glioblastoma and Brain Metastasis in MRI: A Feasibility Study

2021 ◽  
Vol 11 (21) ◽  
pp. 9928
Author(s):  
Hakim Baazaoui ◽  
Simon Hubertus ◽  
Máté E. Maros ◽  
Sherif A. Mohamed ◽  
Alex Förster ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Brain Metastasis ◽  
Metabolic Rate ◽  
Feasibility Study ◽  
Tumor Oxygenation ◽  
Cerebral Metastasis ◽  
Oxygen Extraction Fraction ◽  
Cerebral Metabolic Rate ◽  
Artificial Neural

Glioblastoma may appear similar to cerebral metastasis on conventional MRI in some cases, but their therapies differ significantly. This prospective feasibility study was aimed at differentiating them by applying the quantitative susceptibility mapping and quantitative blood-oxygen-level-dependent (QSM + qBOLD) model to these entities for the first time. We prospectively included 15 untreated patients with glioblastoma (n = 7, median age: 68 years, range: 54–84 years) or brain metastasis (n = 8, median age 66 years, range: 50–78 years) who underwent preoperative MRI including multi-gradient echo and arterial spin labeling sequences. Oxygen extraction fraction (OEF), cerebral blood flow (CBF) and cerebral metabolic rate of oxygen (CMRO2) were calculated in the contrast-enhancing tumor (CET) and peritumoral non-enhancing T2 hyperintense region (NET2), using an artificial neural network. We demonstrated that OEF in CET was significantly lower (p = 0.03) for glioblastomas than metastases, all features were significantly higher (p = 0.01) in CET than in NET2 for metastasis patients only, and the ratios of CET/NET2 for CBF (p = 0.04) and CMRO2 (p = 0.01) were significantly higher in metastasis patients than in glioblastoma patients. Discriminative power of a support-vector machine classifier was highest with a combination of two features, yielding an area under the receiver operating characteristic curve of 0.94 with 93% diagnostic accuracy. QSM + qBOLD allows for robust differentiation of glioblastoma and cerebral metastasis while yielding insights into tumor oxygenation.

Autofluorescence spectroscopy of oral leukoplakia using an artificial neural network

2000 ◽  
Vol 25 (4) ◽  
pp. 325-325
Author(s):  
J.L.N. Roodenburg ◽  
H.J. Van Staveren ◽  
N.L.P. Van Veen ◽  
O.C. Speelman ◽  
J.M. Nauta ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Oral Leukoplakia ◽  
Autofluorescence Spectroscopy ◽  
Artificial Neural

1902: Is Steinstrasse After ESWL of Renal Stones Predictable? Artificial Neural Network Analysis

2004 ◽  
Vol 171 (4S) ◽  
pp. 502-503
Author(s):  
Mohamed A. Gomha ◽  
Khaled Z. Sheir ◽  
Saeed Showky ◽  
Khaled Madbouly ◽  
Emad Elsobky ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Network Analysis ◽  
Renal Stones ◽  
Neural Network Analysis ◽  
Artificial Neural Network Analysis ◽  
Artificial Neural

Forecasting travel demand: a comparison of logit and artificial neural network methods

1998 ◽  
Vol 49 (7) ◽  
pp. 717-722 ◽  
Author(s):  
M C M de Carvalho ◽  
M S Dougherty ◽  
A S Fowkes ◽  
M R Wardman
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Travel Demand ◽  
Network Methods ◽  
Artificial Neural
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