Menu
Diabetic Retinopathy (DR) is a disorder which affects the retina of the human eye which leads to vision loss if not taken notice of and treated accordingly. Hence, regular screening of eyes becomes of utmost importance for diabetics. In this paper, a comprehensive study of the methodologies, their results and limitations have been discussed in order to get the wholesome view. Several image processing techniques were used in the literature to refine the image for maximum information extraction. Comparison of various databases and classifiers used by the researchers revealed that kNN gave better results in terms of accuracy irrespective of number of images used in evaluation. This investigation is specifically useful for the researchers who wish to work in the domain of detection and classification of DR.
[1] https://biomedical-engineering-online.biomedcentral.com/articles/10.1186/s12938-017-0414-z
[2] Chandran, A., Nisha, K. K., & Vineetha, S. (2016). Computer aided approach for proliferative diabetic retinopathy detection in color retinal images. 2016 International Conference on Next Generation Intelligent Systems (ICNGIS).
[3] Herliana, A., Arifin, T., Susanti, S., & Hikmah, A. B. (2018). Feature Selection of Diabetic Retinopathy Disease Using Particle Swarm Optimization and Neural Network. 2018 6th International Conference on Cyber and IT Service Management (CITSM).
[4] Paing, M. P., Choomchuay, S., & Rapeeporn Yodprom, M. D. (2016). Detection of lesions and classification of diabetic retinopathy using fundus images. 2016 9th Biomedical Engineering International Conference (BMEiCON).
[5] 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.
[6] Rahim, S. S., Palade, V., Shuttleworth, J., & Jayne, C. (2014). Automatic Screening and Classification of Diabetic Retinopathy Fundus Images. Communications in Computer and Information Science, 113-122.
[7] Roy, A., Dutta, D., Bhattacharya, P., & Choudhury, S. (2017). Filter and fuzzy c means based feature extraction and classification of diabetic retinopathy using support vector machines. 2017 International Conference on Communication and Signal Processing (ICCSP).
[8] Suryawanshi, V., & Setpal, S. (2017). Guassian transformed GLCM features for classifying diabetic retinopathy. 2017 International Conference on Energy, Communication, Data Analytics and Soft Computing (ICECDS)
[9] Tjandrasa, H., Putra, R. E., Wijaya, A. Y., & Arieshanti, I. (2013). Classification of non-proliferative diabetic retinopathy based on hard exudates using soft margin SVM. 2013 IEEE International Conference on Control System, Computing and Engineering.
[10] Yu, S., Xiao, D., & Kanagasingam, Y. (2017). Exudate detection for diabetic retinopathy with convolutional neural networks. 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).
[11] Decenciere, E., Zhang, X., Cazuguel, G., Lay, B., Cochener, B., Trone, C., … Klein, J.-C. (2014). FEEDBACK ON A PUBLICLY DISTRIBUTED IMAGE DATABASE: THE MESSIDOR DATABASE. Image Analysis & Stereology, 33(3), 231.
[12] T. Kauppi, V. Kalesnykiene, J.-K. Kamarainen, L. Lensu, I. Sorri, A. Raninen, R. Voutilainen, H. Uusitalo, H. Kalvidinen, J. Pietild, Diaretdbl diabetic retinopathy database and evaluation protocol, Proc. Medical Image Understanding and Analysis (MIUA), vol. 2007 (2007).
[13] Ricci, E., & Perfetti, R. (2007). Retinal Blood Vessel Segmentation Using Line Operators and Support Vector Classification. IEEE Transactions on Medical Imaging, 26(10), 1357-1365.
[14] Pal, M. (2005). Random forest classifier for remote sensing classification. International Journal of Remote Sensing, 26(1), 217-222.
[15] Sun J., Du W., Shi N. (2018). A Survey of kNN Algorithm. Information Engineering and Applied Computing, 1-10.
Desh Bhagat University,
Off to NH-44, Amloh Road, Mandi Gobindgarh,
District Fatehgarh Sahib, Punjab- 147301, India