Artificial Neural Networks Based Decision Support System for the Detection of Diabetic Retinopathy

Year 2020, Volume: 24 Issue: 2, 424 - 431, 01.04.2020

Zehra Karapınar Şentürk

https://doi.org/10.16984/saufenbilder.630482

Cited By: 8

Abstract

Machine learning methods have been frequently used for the diagnosis of several diseases recently because of its reliability and convenience. In this paper, a comprehensive overview of the literature related to diabetes and diabetic retinopathy has been done and diagnosis of diabetic retinopathy disease is investigated. Artificial Neural Networks (ANN) method has been applied to the problem using Rapid Miner, a data mining tool. Some other methods have also adapted to the problem, but ANN based detection approach gave the best results. 88.52% sensitivity has been obtained using the features of Messidor dataset. Besides showing the success of ANN in diabetic retinopathy detection, this study also proved that Rapid Miner can be used effectively for the analysis of diabetic retinopathy.

Keywords

diabetic retinopathy, artificial neural networks, Rapid Miner

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.
• O. Y. Okuboyejo, S. Misra, R. Maskeliunas, and R. Damasevicius, “A Neural Network Based Expert System for the Diagnosis of Diabetes Mellitus,” in International Conference on Information Technology Science, 2018, vol. 724, no. February.
• I. Arikpo et al., “Development of a Mobile Software Tool for Diabetes Diagnosis,” vol. 5, no. 3, pp. 1–8, 2018.
• P. Samant and R. Agarwal, “Machine learning techniques for medical diagnosis of diabetes using iris images,” Comput. Methods Programs Biomed., vol. 157, pp. 121–128, 2018.
• D. Sisodia and D. S. Sisodia, “Prediction of Diabetes using Classification Algorithms,” Procedia Comput. Sci., vol. 132, no. Iccids, pp. 1578–1585, 2018.
• Ö. Deperlıoglu and U. Kose, “Diagnosis of Diabetes by Using Deep Neural Network,” in 2nd International Symposium on Multidisciplinary Studies and Innovative Technologies, 2018.
• P. Cao, F. Ren, C. Wan, J. Yang, and O. Zaiane, “Efficient multi-kernel multi-instance learning using weakly supervised and imbalanced data for diabetic retinopathy diagnosis,” Comput. Med. Imaging Graph., vol. 69, pp. 112–124, 2018.
• T. Jemima Jebaseeli, C. Anand Deva Durai, and J. Dinesh Peter, “IOT based sustainable diabetic retinopathy diagnosis system,” Sustain. Comput. Informatics Syst., 2018.
• L. B. Frazao, N. Theera-Umpon, and S. Auephanwiriyakul, “Diagnosis of diabetic retinopathy based on holistic texture and local retinal features,” Inf. Sci. (Ny)., vol. 475, pp. 44–66, 2019.
• A. S. Lundervold and A. Lundervold, “An overview of deep learning in medical imaging focusing on MRI,” Z. Med. Phys., vol. 29, no. 2, pp. 102–127, May 2019.
• A. Esteva et al., “Dermatologist-level classification of skin cancer with deep neural networks,” Nature, vol. 542, no. 7639, pp. 115–118, Feb. 2017.
• W. Zhu, C. Liu, W. Fan, and X. Xie, “DeepLung: Deep 3D dual path nets for automated pulmonary nodule detection and classification,” Proc. - 2018 IEEE Winter Conf. Appl. Comput. Vision, WACV 2018, vol. 2018-Janua, pp. 673–681, 2018.
• S.-H. Wang, P. Phillips, Y. Sui, B. Liu, M. Yang, and H. Cheng, “Classification of Alzheimer’s Disease Based on Eight-Layer Convolutional Neural Network with Leaky Rectified Linear Unit and Max Pooling,” J. Med. Syst., vol. 42, no. 5, p. 85, May 2018.
• V. Gulshan et al., “Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs,” JAMA, vol. 316, no. 22, p. 2402, Dec. 2016.
• J. de la Torre, A. Valls, and D. Puig, “A deep learning interpretable classifier for diabetic retinopathy disease grading,” Neurocomputing, 2019.
• B. Antal and A. Hajdu, “An ensemble-based system for automatic screening of diabetic retinopathy,” Knowledge-Based Syst., vol. 60, pp. 20–27, Apr. 2014.
• “RapidMiner©.” [Online]. Available: https://rapidminer.com/. [Accessed: 04-Mar-2019].
• A. Tharwat, “Independent component analysis: An introduction,” Appl. Comput. Informatics, Aug. 2018.