The DOI matches its Crossref record; 17 of the 17 references it cites were found in a registry or an index.
Artificial Neural Network Model in Stroke Diagnosis
Open accessAbstract
We present a model of Artificial Neural Network (ANN) applied to stroke diagnosis. We use input data related to stroke that serves as inputs for the ANN. These data include clinical symptoms together with stroke risk factors. Each type of data provides input that is evaluated and used during the ANN processing. The adaptive learning algorithm can be used with a plethora of types of medical data and integrated into categorized outputs.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“Mirtskhulava et al used neural network in stroke diagnosis.33”
From Artificial intelligence in healthcare: past, present and future (2017) · cites this paper as [33]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
An introduction to neural computing
Kohonen, T. (1988) · Neural Networks · doi:10.1016/0893-6080(88)90020-2 · Cited by 1,212
Artificial neural networks in medical diagnosis
Amato, F., López, A., Peña-Méndez, E. M., Vaňhara, P., Hampl, A., & Havel, J. (2013) · Journal of Applied Biomedicine · doi:10.2478/v10136-012-0031-x · Cited by 898
Artificial neural networks for diagnosis and survival prediction in colon cancer
Ahmed, F. (2005) · Molecular Cancer · doi:10.1186/1476-4598-4-29 · Cited by 223
Segmentation of multiple sclerosis lesions in MR images: a review
Mortazavi, D., Kouzani, A. Z., & Hamid Soltanian-zadeh, A. (2011) · Neuroradiology · doi:10.1007/s00234-011-0886-7 · Cited by 146
Spelt, L., Andersson, B., Nilsson, J., & Andersson, R. (2011) · European Journal of Surgical Oncology · doi:10.1016/j.ejso.2011.10.013 · Cited by 134
Tuberculosis Disease Diagnosis Using Artificial Neural Networks
Er, O., Temurtaş, F., & Tanrıkulu, A. Ç. (2008) · Journal of Medical Systems · doi:10.1007/s10916-008-9241-x · Cited by 127
Tuberculosis Disease Diagnosis Using Artificial Neural Network Trained with Genetic Algorithm
Elveren, E., & Yumuşak, N. (2009) · Journal of Medical Systems · doi:10.1007/s10916-009-9369-3 · Cited by 98
Computational Intelligence in Early Diabetes Diagnosis: A Review
Shankaracharya, N. A., Odedra, D., Samanta, S., & Vidyarthi, A. S. (2010) · The Review of Diabetic Studies · doi:10.1900/rds.2010.7.252 · Cited by 93
Artificial neural networks in pancreatic disease
Bartosch-Härlid, A., Andersson, B., Aho, U., Nilsson, J., & Andersson, R. E. (2008) · British journal of surgery · doi:10.1002/bjs.6239 · Cited by 78
Artificial neural network in diagnosis of metastatic carcinoma in effusion cytology
Barwad, A. W., Dey, P., & Susheilia, S. (2011) · Cytometry Part B Clinical Cytometry · doi:10.1002/cyto.b.20632 · Cited by 59
Barbosa, D. C., Roupar, D., Ramos, J., Tavares, A., & Lima, C. (2012) · BioMedical Engineering OnLine · doi:10.1186/1475-925x-11-3 · Cited by 52
A fast and adaptive automated disease diagnosis method with an innovative neural network model
Alkım, E., Gürbüz, E., & Kılıç, E. (2012) · Neural Networks · doi:10.1016/j.neunet.2012.04.010 · Cited by 45
Artificial Neural Networks Based Controller for Glucose Monitoring during Clamp Test
Catalogna, M., Cohen, E., Fishman, S., Halpern, Z., Nevo, U., & Ben‐Jacob, E. (2012) · PLoS ONE · doi:10.1371/journal.pone.0044587 · Cited by 41
Application of an artificial neural network in the prognosis of chronic myeloid leukemia.
Dey, P., Lamba, A., Kumari, S., & Marwaha, N. (2011) · PubMed · Cited by 31
Designing neural networks to improve timing performances of intelligent controllers
Pelusi, D. (2013) · Journal of Discrete Mathematical Sciences and Cryptography · doi:10.1080/09720529.2013.821333 · Cited by 19
Analysis of the "GPATD": Geometrical Influence on Blood Clot Extraction Using CFD Simulation
Romero, G., Martínez, M. L., Pearce, G., et al. (2014) · UKSim-AMSS International Conference on Computer Modelling and Simulation (UKSim) · doi:10.1109/uksim.2014.100 · Cited by 2
Self-citationStroke Monitoring using Smartphone Sensors and Artificial Neural Network Soft Sensors
Mirtskhulava, L., Al-Majeed, S. S., Pearce, G., et al. (2015) · Lincoln Repository (University of Lincoln) · Cited by 1
Self-citation
Research works printed but not linked: 3
- 0, What is a feedforward ne html
- 10.2478/v10136-012-0031-x · DOI · Paper page
- hacke, 2008, M D for the ECASS Investigators Thrombolysis with Alteplase 3 to 4 5 Hours after Acute Ischemic Stroke N Engl J Med 2008, 359, 1317
Cited by
Sorted by citations
Artificial intelligence in healthcare: past, present and future
Jiang, F., Jiang, Y., Zhi, H., Dong, Y., Li, H., Ma, S., Wang, Y., Dong, Q., Shen, H., & Wang, Y. (2017) · Stroke and Vascular Neurology · doi:10.1136/svn-2017-000101 · Cited by 4,837
Metadata
- Authors
- Mirtskhulava, L., Wong, J., Al-Majeed, S., & Pearce, G.
- Affiliations
- Tbilisi State University (GE)
- Military Technological College (OM)
- National University Heart Centre Singapore (SG)
- Aston University (GB)
- Venue
- 2015 17th UKSim-AMSS International Conference on Modelling and Simulation (UKSim)
- Publisher
- IEEE
- Published
- 2015-03
- Pages
- 50-53
- OpenAlex
- W2490950022
- Cited by
- 19
- References
- 17
- Citations by year
- 2023: 1 · 2022: 2 · 2021: 2 · 2020: 5 · 2019: 5 · 2018: 1 · 2017: 3
- FWCI
- 2.29
- Type
- conference paper
- Language
- en
- Open access
- green
- eprints.glos.ac.uk
- Locations
- UKSim International Conference on Computer Modelling and Simulation (UKSim) (publishedVersion)
- Research Repository (University of Gloucestershire) (submittedVersion)
- Lincoln Repository (University of Lincoln) (submittedVersion)
- Topics
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Health Sciences › Medicine › Clinical Neurology › Acute Ischemic Stroke Management
- Physical Sciences › Computer Science › Artificial Intelligence › Neural Networks and Applications
- Keywords
- stroke diagnosis, artificial neural networks, clinical symptoms, stroke risk factors, adaptive learning, medical data classification
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
