A Review of Recent Studies on Diagnosing Diabetic Retinopathy by Artificial Intelligence
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Published
Jul 29, 2019
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Tolga Tukel
Utku Kose
http://orcid.org/0000-0002-9652-6415
http://orcid.org/0000-0002-9652-6415
Abstract
Currently, Artificial Intelligence based solutions are widely used in different fields. Because of need for adaptive and strong solutions for vital, real world problems, intelligent systems developed with Artificial Intelligence approaches are often used by researchers in especially critical fields of the humankind. The field of medical is known as one of them and there is a great trend in applying intelligent systems for solving different problems of the medical. Medical diagnosis is one of the most popular problem scope in this manner and machine learning related research efforts have been performed for all different kinds of diseases. Objective of this study is to consider the disease of diabetic retinography and provide a recent, wide enough review of the associated literature of Artificial Intelligence for effective diagnosis of diabetic retinography. The study briefly gives information about the background and enables readers to have recent information about what have been done with Artificial Intelligence based techniques for diagnosing diabetic retinography.
Article Details
How to Cite
TUKEL, Tolga; KOSE, Utku.
A Review of Recent Studies on Diagnosing Diabetic Retinopathy by Artificial Intelligence.
Journal of Multidisciplinary Developments, [S.l.], v. 4, n. 1, p. 23-29, july 2019.
ISSN 2564-6095.
Available at: <http://www.jomude.com/index.php/jomude/article/view/66>. Date accessed: 21 jan. 2025.
Section
Natural Sciences - Regular Research Paper
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References
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Al-Shayea, Q. K. (2011). Artificial neural networks in medical diagnosis. International Journal of Computer Science Issues, 8(2), 150-154.
Barr, A., & Feigenbaum, E. A. (Eds.). (2014). The handbook of artificial intelligence (Vol. 2). Butterworth-Heinemann.
Bonabeau, E., Marco, D. D. R. D. F., Dorigo, M., & Theraulaz, G. (1999). Swarm intelligence: from natural to artificial systems (No. 1). Oxford university press.
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Deperlioglu, O. (2018). Classification of phonocardiograms with convolutional neural networks. BRAIN. Broad Research in Artificial Intelligence and Neuroscience, 9(2), 22-33.
Deperlioglu, O. (2018). Segmentation of heart sounds by re-sampled signal energy method. BRAIN. Broad Research in Artificial Intelligence and Neuroscience, 9(1), 17-28.
Deperlioglu, O., & Kose, U. (2011). An educational tool for artificial neural networks. Computers & Electrical Engineering, 37(3), 392-402.
Deperlioglu, O., & Kose, U. (2018). Practical Method for the Underwater Image Enhancement with Adjusted CLAHE. In 2018 International Conference on Artificial Intelligence and Data Processing (IDAP) (pp. 1-6). IEEE.
Gargeya, R., & Leng, T. (2017). Automated identification of diabetic retinopathy using deep learning. Ophthalmology, 124(7), 962-969.
Genesereth, M. R., & Nilsson, N. J. (2012). Logical foundations of artificial intelligence. Morgan Kaufmann.
Gulshan, V., Peng, L., Coram, M., Stumpe, M. C., Wu, D., Narayanaswamy, A., ... & Kim, R. (2016). Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. Jama, 316(22), 2402-2410.
Guraksin, G. E., Ergun, U., & Deperlioglu, O. (2009). Performing discrete fourier transform of the heart sounds on the pocket computer. In 2009 14th National Biomedical Engineering Meeting (pp. 1-4). IEEE.
Guraksin, G. E., Kose, U., & Deperlioglu, O. (2016). Underwater image enhancement based on contrast adjustment via differential evolution algorithm. In 2016 International Symposium on INnovations in Intelligent SysTems and Applications (INISTA) (pp. 1-5). IEEE.
Hassanien, A. E., & Emary, E. (2018). Swarm intelligence: principles, advances, and applications. CRC Press.
Hemanth, D. J., Deperlioglu, O., & Kose, U. (2019). An enhanced diabetic retinopathy detection and classification approach using deep convolutional neural network. Neural Computing and Applications, 1-15.
Hemanth, J. D., Kose, U., Deperlioglu, O., & de Albuquerque, V. H. C. (2018). An augmented reality-supported mobile application for diagnosis of heart diseases. The Journal of Supercomputing, 1-26.
Hengstler, M., Enkel, E., & Duelli, S. (2016). Applied artificial intelligence and trust—The case of autonomous vehicles and medical assistance devices. Technological Forecasting and Social Change, 105, 105-120.
Kononenko, I. (2001). Machine learning for medical diagnosis: history, state of the art and perspective. Artificial Intelligence in medicine, 23(1), 89-109.
Kose, U. (2012). Design and development of a software system for swarm intelligence based research studies. BRAIN. Broad Research in Artificial Intelligence and Neuroscience, 3(3), 12-17.
Kose, U. (2015). Present state of swarm intelligence and future directions. In Encyclopedia of Information Science and Technology, Third Edition (pp. 239-252). IGI Global.
Kose, U. (2018). An ant-lion optimizer-trained artificial neural network system for chaotic electroencephalogram (EEG) prediction. Applied Sciences, 8(9), 1613.
Kose, U. (2018). Are we safe enough in the future of artificial intelligence? A discussion on machine ethics and artificial intelligence safety. BRAIN. Broad Research in Artificial Intelligence and Neuroscience, 9(2), 184-197.
Kose, U. (2018). Towards an Intelligent Biomedical Engineering With Nature-Inspired Artificial Intelligence Techniques. In Nature-Inspired Intelligent Techniques for Solving Biomedical Engineering Problems (pp. 1-26). IGI Global.
Kose, U. (2019). Using Artificial Intelligence Techniques for Economic Time Series Prediction. In Contemporary Issues in Behavioral Finance (pp. 13-28). Emerald Publishing Limited.
Kose, U., & Arslan, A. (2014). An Adaptive Neuro-Fuzzy Inference System-Based Approach to Forecast Time Series of Chaotic Systems. In Chaos, Complexity and Leadership 2012 (pp. 17-22). Springer, Dordrecht.
Kose, U., & Arslan, A. (2014). Chaotic systems and their recent implementations on improving intelligent systems. In Handbook of Research on Novel Soft Computing Intelligent Algorithms: Theory and Practical Applications (pp. 69-101). IGI Global.
Kose, U., & Arslan, A. (2017). Forecasting chaotic time series via anfis supported by vortex optimization algorithm: Applications on electroencephalogram time series. Arabian Journal for Science and Engineering, 42(8), 3103-3114.
Kose, U., & Arslan, A. (2017). Optimization of self‐learning in Computer Engineering courses: An intelligent software system supported by Artificial Neural Network and Vortex Optimization Algorithm. Computer Applications in Engineering Education, 25(1), 142-156.
Kose, U., & Arslan, A. (2019). Time series prediction with a hybrid system formed by artificial neural network and cognitive development optimization algorithm. Scientia Iranica. Transaction E, Industrial Engineering, 26(2), 942-958.
Kose, U., & Deperlioglu, O. (2012). Intelligent learning environments within blended learning for ensuring effective c programming course. arXiv preprint arXiv:1205.2670.
Kose, U., & Sert, S. (2017). Improving Content Marketing Processes With The Approaches By Artificial Intelligence. EcoForum, 6(1), 1-31.
Kose, U., & Vasant, P. (2018). A Model of Swarm Intelligence Based Optimization Framework Adjustable According to Problems. In Innovative Computing, Optimization and Its Applications (pp. 21-38). Springer, Cham.
Kose, U., Guraksin, G. E., & Deperlioglu, O. (2015). Diabetes determination via vortex optimization algorithm based support vector machines. In 2015 Medical Technologies National Conference (TIPTEKNO) (pp. 1-4). IEEE.
Neill, D. B. (2013). Using artificial intelligence to improve hospital inpatient care. IEEE Intelligent Systems, 28(2), 92-95.
Pavaloiu, A., & Kose, U. (2017). Ethical Artificial Intelligence-An Open Question. Journal of Multidisciplinary Developments, 2(2), 15-27.
Pratt, H., Coenen, F., Broadbent, D. M., Harding, S. P., & Zheng, Y. (2016). Convolutional neural networks for diabetic retinopathy. Procedia Computer Science, 90, 200-205.
Rajalakshmi, R., Subashini, R., Anjana, R. M., & Mohan, V. (2018). Automated diabetic retinopathy detection in smartphone-based fundus photography using artificial intelligence. Eye, 32(6), 1138.
Santosh, K. C., Antani, S., Guru, D. S., & Dey, N. (2019). Medical Imaging: Artificial Intelligence, Image Recognition, and Machine Learning Techniques. CRC Press.
Sargent, D. J. (2001). Comparison of artificial neural networks with other statistical approaches: results from medical data sets. Cancer: Interdisciplinary International Journal of the American Cancer Society, 91(S8), 1636-1642.
Saucedo, J. A. M., Hemanth, J. D., & Kose, U. (2019). Prediction of electroencephalogram time series with electro-search optimization algorithm trained adaptive neuro-fuzzy inference system. IEEE Access, 7, 15832-15844.
Sra, S., Nowozin, S., & Wright, S. J. (Eds.). (2012). Optimization for machine learning. MIT Press.
Takahashi, H., Tampo, H., Arai, Y., Inoue, Y., & Kawashima, H. (2017). Applying artificial intelligence to disease staging: Deep learning for improved staging of diabetic retinopathy. PloS one, 12(6), e0179790.
Tezel, G., & Kose, U. (2011). Headache disease diagnosis by using the clonal selection algorithm. In 6th International Advanced Technologies Symposium (pp. 144-148).
Ting, D. S. W., Cheung, C. Y. L., Lim, G., Tan, G. S. W., Quang, N. D., Gan, A., ... & Wong, E. Y. M. (2017). Development and validation of a deep learning system for diabetic retinopathy and related eye diseases using retinal images from multiethnic populations with diabetes. Jama, 318(22), 2211-2223.
Vasant, P., Kose, U., & Watada, J. (2017). Metaheuristic techniques in enhancing the efficiency and performance of thermo-electric cooling devices. Energies, 10(11), 1703.
Vidal-Alaball, J., Fibla, D. R., Zapata, M. A., Marin-Gomez, F. X., & Fernandez, O. S. (2019). Artificial Intelligence for the Detection of Diabetic Retinopathy in Primary Care: Protocol for Algorithm Development. JMIR research protocols, 8(2), e12539.
Wong, T. Y., & Bressler, N. M. (2016). Artificial intelligence with deep learning technology looks into diabetic retinopathy screening. Jama, 316(22), 2366-2367.
Xu, K., Feng, D., & Mi, H. (2017). Deep convolutional neural network-based early automated detection of diabetic retinopathy using fundus image. Molecules, 22(12), 2054.
Alpaydin, E. (2016). Machine learning: the new AI. MIT Press.
Al-Shayea, Q. K. (2011). Artificial neural networks in medical diagnosis. International Journal of Computer Science Issues, 8(2), 150-154.
Barr, A., & Feigenbaum, E. A. (Eds.). (2014). The handbook of artificial intelligence (Vol. 2). Butterworth-Heinemann.
Bonabeau, E., Marco, D. D. R. D. F., Dorigo, M., & Theraulaz, G. (1999). Swarm intelligence: from natural to artificial systems (No. 1). Oxford university press.
Boz, H., & Kose, U. (2018). Emotion Extraction from Facial Expressions by Using Artificial Intelligence Techniques. BRAIN. Broad Research in Artificial Intelligence and Neuroscience, 9(1), 5-16.
Deperlioglu, O. (2018). Classification of phonocardiograms with convolutional neural networks. BRAIN. Broad Research in Artificial Intelligence and Neuroscience, 9(2), 22-33.
Deperlioglu, O. (2018). Segmentation of heart sounds by re-sampled signal energy method. BRAIN. Broad Research in Artificial Intelligence and Neuroscience, 9(1), 17-28.
Deperlioglu, O., & Kose, U. (2011). An educational tool for artificial neural networks. Computers & Electrical Engineering, 37(3), 392-402.
Deperlioglu, O., & Kose, U. (2018). Practical Method for the Underwater Image Enhancement with Adjusted CLAHE. In 2018 International Conference on Artificial Intelligence and Data Processing (IDAP) (pp. 1-6). IEEE.
Gargeya, R., & Leng, T. (2017). Automated identification of diabetic retinopathy using deep learning. Ophthalmology, 124(7), 962-969.
Genesereth, M. R., & Nilsson, N. J. (2012). Logical foundations of artificial intelligence. Morgan Kaufmann.
Gulshan, V., Peng, L., Coram, M., Stumpe, M. C., Wu, D., Narayanaswamy, A., ... & Kim, R. (2016). Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. Jama, 316(22), 2402-2410.
Guraksin, G. E., Ergun, U., & Deperlioglu, O. (2009). Performing discrete fourier transform of the heart sounds on the pocket computer. In 2009 14th National Biomedical Engineering Meeting (pp. 1-4). IEEE.
Guraksin, G. E., Kose, U., & Deperlioglu, O. (2016). Underwater image enhancement based on contrast adjustment via differential evolution algorithm. In 2016 International Symposium on INnovations in Intelligent SysTems and Applications (INISTA) (pp. 1-5). IEEE.
Hassanien, A. E., & Emary, E. (2018). Swarm intelligence: principles, advances, and applications. CRC Press.
Hemanth, D. J., Deperlioglu, O., & Kose, U. (2019). An enhanced diabetic retinopathy detection and classification approach using deep convolutional neural network. Neural Computing and Applications, 1-15.
Hemanth, J. D., Kose, U., Deperlioglu, O., & de Albuquerque, V. H. C. (2018). An augmented reality-supported mobile application for diagnosis of heart diseases. The Journal of Supercomputing, 1-26.
Hengstler, M., Enkel, E., & Duelli, S. (2016). Applied artificial intelligence and trust—The case of autonomous vehicles and medical assistance devices. Technological Forecasting and Social Change, 105, 105-120.
Kononenko, I. (2001). Machine learning for medical diagnosis: history, state of the art and perspective. Artificial Intelligence in medicine, 23(1), 89-109.
Kose, U. (2012). Design and development of a software system for swarm intelligence based research studies. BRAIN. Broad Research in Artificial Intelligence and Neuroscience, 3(3), 12-17.
Kose, U. (2015). Present state of swarm intelligence and future directions. In Encyclopedia of Information Science and Technology, Third Edition (pp. 239-252). IGI Global.
Kose, U. (2018). An ant-lion optimizer-trained artificial neural network system for chaotic electroencephalogram (EEG) prediction. Applied Sciences, 8(9), 1613.
Kose, U. (2018). Are we safe enough in the future of artificial intelligence? A discussion on machine ethics and artificial intelligence safety. BRAIN. Broad Research in Artificial Intelligence and Neuroscience, 9(2), 184-197.
Kose, U. (2018). Towards an Intelligent Biomedical Engineering With Nature-Inspired Artificial Intelligence Techniques. In Nature-Inspired Intelligent Techniques for Solving Biomedical Engineering Problems (pp. 1-26). IGI Global.
Kose, U. (2019). Using Artificial Intelligence Techniques for Economic Time Series Prediction. In Contemporary Issues in Behavioral Finance (pp. 13-28). Emerald Publishing Limited.
Kose, U., & Arslan, A. (2014). An Adaptive Neuro-Fuzzy Inference System-Based Approach to Forecast Time Series of Chaotic Systems. In Chaos, Complexity and Leadership 2012 (pp. 17-22). Springer, Dordrecht.
Kose, U., & Arslan, A. (2014). Chaotic systems and their recent implementations on improving intelligent systems. In Handbook of Research on Novel Soft Computing Intelligent Algorithms: Theory and Practical Applications (pp. 69-101). IGI Global.
Kose, U., & Arslan, A. (2017). Forecasting chaotic time series via anfis supported by vortex optimization algorithm: Applications on electroencephalogram time series. Arabian Journal for Science and Engineering, 42(8), 3103-3114.
Kose, U., & Arslan, A. (2017). Optimization of self‐learning in Computer Engineering courses: An intelligent software system supported by Artificial Neural Network and Vortex Optimization Algorithm. Computer Applications in Engineering Education, 25(1), 142-156.
Kose, U., & Arslan, A. (2019). Time series prediction with a hybrid system formed by artificial neural network and cognitive development optimization algorithm. Scientia Iranica. Transaction E, Industrial Engineering, 26(2), 942-958.
Kose, U., & Deperlioglu, O. (2012). Intelligent learning environments within blended learning for ensuring effective c programming course. arXiv preprint arXiv:1205.2670.
Kose, U., & Sert, S. (2017). Improving Content Marketing Processes With The Approaches By Artificial Intelligence. EcoForum, 6(1), 1-31.
Kose, U., & Vasant, P. (2018). A Model of Swarm Intelligence Based Optimization Framework Adjustable According to Problems. In Innovative Computing, Optimization and Its Applications (pp. 21-38). Springer, Cham.
Kose, U., Guraksin, G. E., & Deperlioglu, O. (2015). Diabetes determination via vortex optimization algorithm based support vector machines. In 2015 Medical Technologies National Conference (TIPTEKNO) (pp. 1-4). IEEE.
Neill, D. B. (2013). Using artificial intelligence to improve hospital inpatient care. IEEE Intelligent Systems, 28(2), 92-95.
Pavaloiu, A., & Kose, U. (2017). Ethical Artificial Intelligence-An Open Question. Journal of Multidisciplinary Developments, 2(2), 15-27.
Pratt, H., Coenen, F., Broadbent, D. M., Harding, S. P., & Zheng, Y. (2016). Convolutional neural networks for diabetic retinopathy. Procedia Computer Science, 90, 200-205.
Rajalakshmi, R., Subashini, R., Anjana, R. M., & Mohan, V. (2018). Automated diabetic retinopathy detection in smartphone-based fundus photography using artificial intelligence. Eye, 32(6), 1138.
Santosh, K. C., Antani, S., Guru, D. S., & Dey, N. (2019). Medical Imaging: Artificial Intelligence, Image Recognition, and Machine Learning Techniques. CRC Press.
Sargent, D. J. (2001). Comparison of artificial neural networks with other statistical approaches: results from medical data sets. Cancer: Interdisciplinary International Journal of the American Cancer Society, 91(S8), 1636-1642.
Saucedo, J. A. M., Hemanth, J. D., & Kose, U. (2019). Prediction of electroencephalogram time series with electro-search optimization algorithm trained adaptive neuro-fuzzy inference system. IEEE Access, 7, 15832-15844.
Sra, S., Nowozin, S., & Wright, S. J. (Eds.). (2012). Optimization for machine learning. MIT Press.
Takahashi, H., Tampo, H., Arai, Y., Inoue, Y., & Kawashima, H. (2017). Applying artificial intelligence to disease staging: Deep learning for improved staging of diabetic retinopathy. PloS one, 12(6), e0179790.
Tezel, G., & Kose, U. (2011). Headache disease diagnosis by using the clonal selection algorithm. In 6th International Advanced Technologies Symposium (pp. 144-148).
Ting, D. S. W., Cheung, C. Y. L., Lim, G., Tan, G. S. W., Quang, N. D., Gan, A., ... & Wong, E. Y. M. (2017). Development and validation of a deep learning system for diabetic retinopathy and related eye diseases using retinal images from multiethnic populations with diabetes. Jama, 318(22), 2211-2223.
Vasant, P., Kose, U., & Watada, J. (2017). Metaheuristic techniques in enhancing the efficiency and performance of thermo-electric cooling devices. Energies, 10(11), 1703.
Vidal-Alaball, J., Fibla, D. R., Zapata, M. A., Marin-Gomez, F. X., & Fernandez, O. S. (2019). Artificial Intelligence for the Detection of Diabetic Retinopathy in Primary Care: Protocol for Algorithm Development. JMIR research protocols, 8(2), e12539.
Wong, T. Y., & Bressler, N. M. (2016). Artificial intelligence with deep learning technology looks into diabetic retinopathy screening. Jama, 316(22), 2366-2367.
Xu, K., Feng, D., & Mi, H. (2017). Deep convolutional neural network-based early automated detection of diabetic retinopathy using fundus image. Molecules, 22(12), 2054.