Diabetic Retinopathy Detection Techniques: A Review
Keywords:
Diabetic Retinopathy, Feature Extraction, Classification, SegmentationAbstract
The information that is stored within the pixels of images is processed through image processing technology. For detecting different kinds of diseases, the medical image processing Based approach is applied. The diabetes patients might suffer from a disease in which certain spots are created on the eyes of patients. The approach through which this disease known as diabetic retinopathy can be detected is based on image processing. There are two phases in which the diabetes retinopathy can be detected through image processing. Feature extraction is the initial phase of this approach. Classification is applied in the second phase of this technique. In this paper, various diabetic retinopathy detection techniques will be reviewed in terms of various parameters
Downloads
References
Dutta MK, ParthaSarathi M, Ganguly S, Ganguly S, Srivastava K, “An efficient image processing-based technique for comprehensive detection and grading of non-proliferative diabetic retinopathy from fundus images”, 2017, Comput Methods Biomech Biomed Eng Imaging Vis 5(3):195–207
V. Kumar, T. Lal, P. Dhuliya, and Diwaker Pant, “A study and comparison of different image segmentation algorithms”, In Advances in Computing, Communication, & Automation (ICACCA)(Fall), International Conference on, IEEE 2016, pp. 1-6
R. Radha, and S. Jeyalakshmi, “An effective algorithm for edges and veins detection in leaf images”, In Computing and Communication Technologies (WCCCT), 2014 World Congress on, IEEE 2014, pp. 128-131
P. Gupta, “A Survey of Techniques and Applications For Real Time Image Processing”, Journal of Global Research in Computer Science (UGC Approved Journal) 4, no. 8 (2013): 30-39
KhinYadanar Win, SomsakChoomchuay, “Automated detection of exudates using histogram analysis for Digital Retinal Images”, IEEE Conference, 2016 International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS), 24-27 Oct. 2016.
Jiri Gazarek, Jiri Jan, Radim Kolar, Jan Odstrcilik, “Retinal nerve fibre layer detection in fundus camera images compared to results from optical coherence tomography”, IEEE Conference, 2011 International Conference on Image Information Processing, 3-5 Nov. 2011
Yash S. Boral, Snehal S. Thorat, “Classification of Diabetic Retinopathy based on Hybrid Neural Network”, 2021, 5th International Conference on Computing Methodologies and Communication (ICCMC)
Md. Mahmudul Hasan Sabbir, Abu Sayeed, Md. Ahsan-Uz-Zaman Jamee, “Diabetic Retinopathy Detection using Texture Features and Ensemble Learning”, 2020, IEEE Region 10 Symposium (TENSYMP)
Narjes Karami, Hossein Rabbani, “A dictionary learning based method for detection of diabetic retinopathy in color fundus images”, 2017, 10th Iranian Conference on Machine Vision and Image Processing (MVIP)
Anas Bilal, Guangmin Sun, Yu Li, Sarah Mazhar, Abdul Qadir Khan, “Diabetic Retinopathy Detection and Classification Using Mixed Models for a Disease Grading Database”, 2021, IEEE Access
Elaouaber Zineb Aziza, Lazouni Mohamed El Amine, Messadi Mohamed, Bessaid Abdelhafid, “Decision tree CART algorithm for diabetic retinopathy classification”, 2019, 6th International Conference on Image and Signal Processing and their Applications (ISPA)
Arwa Gamal Eldin, Mohammed Mustafa, Rihab Eltayeb, Fragoon Mohamed, “Automatic Detection of Diabetic Retinopathy using Neural Networks and Support Vector Machine”, 2020, International Conference on Computing and Information Technology (ICCIT-1441)
Pan Junjun, Yong Zhifan, Sui Dong, Qin Hong, “Diabetic Retinopathy Detection Based on Deep Convolutional Neural Networks for Localization of Discriminative Regions”, 2018, International Conference on Virtual Reality and Visualization (ICVRV)
K. Shankar, Yizhuo Zhang, Yiwei Liu, Ling Wu, Chi-Hua Chen, “Hyperparameter Tuning Deep Learning for Diabetic Retinopathy Fundus Image Classification”, 2020, IEEE Access
Tahira Nazir, Aun Irtaza, Junaid Rashid, Marriam Nawaz, Toqeer Mehmood, “Diabetic Retinopathy Lesions Detection using Faster-RCNN from retinal images”, 2020, First International Conference of Smart Systems and Emerging Technologies (SMARTTECH)
Enrique V. Carrera, Andrés González, Ricardo Carrera, “Automated detection of diabetic retinopathy using SVM”, 2017, IEEE XXIV International Conference on Electronics, Electrical Engineering and Computing (INTERCON)
C. Jayakumari, Vidhya Lavanya, E P Sumesh, “Automated Diabetic Retinopathy Detection and classification using ImageNet Convolution Neural Network using Fundus Images”, 2020, International Conference on Smart Electronics and Communication (ICOSEC)
Navoneel Chakrabarty, Subhrasankar Chatterjee, “An Offbeat Technique for Diabetic Retinopathy Detection using Computer Vision”, 2019, 10th International Conference on Computing, Communication and Networking Technologies (ICCCNT)
Dinial Utami Nurul Qomariah, Handayani Tjandrasa, Chastine Fatichah, “Classification of Diabetic Retinopathy and Normal Retinal Images using CNN and SVM”, 2019, 12th International Conference on Information & Communication Technology and System (ICTS)
Alejandro Garcia, Machine Learning for Customer Segmentation and Targeted Marketing , Machine Learning Applications Conference Proceedings, Vol 3 2023.
Paul Garcia, Ian Martin, Laura López, Sigurðsson Ólafur, Matti Virtanen. Predictive Analytics in Education: Leveraging Machine Learning for Student Success. Kuwait Journal of Machine Learning, 2(1). Retrieved from http://kuwaitjournals.com/index.php/kjml/article/view/164
Sharma, R., & Dhabliya, D. (2019). Attacks on transport layer and multi-layer attacks on manet. International Journal of Control and Automation, 12(6 Special Issue), 5-11. Retrieved from www.scopus.com
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.
IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.
