Artificial Neural Networks Based Decision Support System for the Detection of Diabetic Retinopathy
Abstract
Keywords
References
- B. E. R. Gaillard and D. K. Karumanchi, “Trends in early diabetes diagnosis,” Ophthalmol. Manag., vol. 18, no. November 2015, pp. 28–30, 2015.
- M. Dansinger, “Types of Diabetes Mellitus,” WebMD Medical Reference, 2017. [Online]. Available: https://www.webmd.com/diabetes/guide/types-of-diabetes-mellitus#3. [Accessed: 15-Jan-2019].
- K. Kayaer and T. Yildirim, “Medical diagnosis on Pima Indian diabetes using general regression neural networks,” in Proceedings of the international conference on artificial neural networks and neural information processing (ICANN/ICONIP), 2003, pp. 181–184.
- J. Han, J. C. Rodriguze, and M. Beheshti, “Diabetes data analysis and prediction model discovery using RapidMiner,” in 2nd International Conference on Future Generation Communication and Networking, 2008, vol. 3, pp. 96–99.
- V. V. Vijayan and C. Anjali, “Prediction and diagnosis of diabetes mellitus - A machine learning approach,” in IEEE Recent Advances in Intelligent Computational Systems, RAICS 2015, 2015, no. December, pp. 122–127.
- H. S. Bilge and Y. Kerimbekov, “Classification with Lorentzian distance metric,” in 23rd Signal Processing and Communications Applications Conference, 2015, pp. 1–4.
- M. S. Kurt and T. Ensari, “Diabet diagnosis with support vector machines and multi layer perceptron,” in Electric Electronics, Computer Science, Biomedical Engineerings’ Meeting (EBBT), 2017, pp. 1–4.
- D. Choubey, S. Paul, S. Kumar, and S. Kumar, “Classification of Pima indian diabetes dataset using naive bayes with genetic algorithm as an attribute selection,” Commun. Comput. Syst., pp. 451–455, 2017.
Details
Primary Language
English
Subjects
Artificial Intelligence
Journal Section
Research Article
Authors
Publication Date
April 1, 2020
Submission Date
October 7, 2019
Acceptance Date
March 11, 2020
Published in Issue
Year 2020 Volume: 24 Number: 2
Cited By
Experimental investigation and prediction of performance and emission responses of a CI engine fuelled with different metal-oxide based nanoparticles–diesel blends using different machine learning algorithms
Energy
https://doi.org/10.1016/j.energy.2020.119076Electricity production based forecasting of greenhouse gas emissions in Turkey with deep learning, support vector machine and artificial neural network algorithms
Journal of Cleaner Production
https://doi.org/10.1016/j.jclepro.2020.125324Diyabetik Retinopati Teşhisi için Fundus Görüntülerinin Derin Öğrenme Tabanlı Sınıflandırılması
European Journal of Science and Technology
https://doi.org/10.31590/ejosat.1011806Determination of Metabolic Rate from Physical Measurements of Heart Rate, Mean Skin Temperature and Carbon Dioxide Variation
Sakarya University Journal of Science
https://doi.org/10.16984/saufenbilder.981511Layer recurrent neural network-based diagnosis of Parkinson’s disease using voice features
Biomedical Engineering / Biomedizinische Technik
https://doi.org/10.1515/bmt-2022-0022Deep Retinal Image Analysis and Classification Using Deer Hunting Optimization-Based Tandem Pulse Coupled Neural Network
Journal of The Institution of Engineers (India): Series B
https://doi.org/10.1007/s40031-022-00785-9Machine Learning Based Classification for Spam Detection
Sakarya University Journal of Science
https://doi.org/10.16984/saufenbilder.1264476Machine and deep learning techniques for the prediction of diabetics: a review
Multimedia Tools and Applications
https://doi.org/10.1007/s11042-024-19766-9