Diabetic Retinopathy Detection Using Optimized Random Under Sampling (ORUS) Algorithm
DOI:
https://doi.org/10.32628/IJSRST52310275Keywords:
Diabetic retinopathy, insulin, pancreas, optimized RUS boost algorithm.Abstract
This paper provides the detection of diabetic retinopathy (DR) from the different features, the dataset is a combination of both proliferated and non-proliferated diabetic retinopathy. The people who at the age of above 60 and having the diabetes will face this diabetic retinopathy problem. This is mainly occurred due to sugar builds up on the blood vessels. This is because of pancreas doesn’t produce proper level of insulin. The early detection of diabetic retinopathy plays very important role, with this early detection it can be able to reduce the level of severity, after that by taking proper medications and all it can be able to control. In this kind of detection process every feature is very important, with these features it can be able to classify whether the person is diabetic retinopathy prone or not. To detect the diabetic retinopathy many algorithms are used. To increase the sensitivity and to reduce error rate, by using optimized RUS boost algorithm.
References
- U. Ahmed et al., "Prediction of Diabetes Empowered With Fused Machine Learning," in IEEE Access, vol. 10, pp. 8529-8538, 2022, doi: 10.1109/ACCESS.2022.3142097.
- L. Qiao, Y. Zhu and H. Zhou, "Diabetic Retinopathy Detection Using Prognosis of Microaneurysm and Early Diagnosis System for Non-Proliferative Diabetic Retinopathy Based on Deep Learning Algorithms," in IEEE Access, vol. 8, pp. 104292-104302, 2020, doi: 10.1109/ACCESS.2020.2993937.
- S. Roychowdhury, D. D. Koozekanani and K. K. Parhi, "Automated detection of neovascularization for proliferative diabetic retinopathy screening," 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Orlando, FL, USA, 2016, pp. 1300-1303, doi: 10.1109/EMBC.2016.7590945.
- Weiss, Gary M. et al. “Cost-Sensitive Learning vs. Sampling: Which is Best for Handling Unbalanced Classes with Unequal Error Costs?” International Conference on Data Mining (2007).
- N. V. Chawla, D. A. Cieslak, L. O. Hall, and A. Joshi, “Automatically countering imbalance and its empirical relationship to cost,” Data Mining Knowl. Discov., vol. 17, no. 2, pp. 225–252, Oct. 2008.
- Roychowdhury S, Koozekanani DD, Parhi KK. Automated detection of neovascularization for proliferative diabetic retinopathy screening. Annu Int Conf IEEE Eng Med Biol Soc. 2016 Aug;2016:1300-1303. doi: 10.1109/EMBC.2016.7590945. PMID: 28268564.
- S. Kumar and B. Kumar, "Diabetic Retinopathy Detection by Extracting Area and Number of Microaneurysm from Colour Fundus Image," 2018 5th International Conference on Signal Processing and Integrated Networks (SPIN), Noida, India, 2018, pp. 359-364, doi: 10.1109/SPIN.2018.8474264.
- N. Singh, L. Kaur, and K. Singh, ‘‘Histogram equalization techniques for enhancement of low radiance retinal images for early detection of diabetic retinopathy,’’ Eng. Sci. Technol., Int. J., vol. 22, no. 3, pp. 736–745, Jun. 2019
- ] L. Tang, M. Niemeijer, J. M. Reinhardt, M. K. Garvin, and M. D. Abramoff, ‘‘Splat feature classification with application to retinal hemorrhage detection in fundus images,’’ IEEE Trans. Med. Imag., vol. 32, no. 2, pp. 364–375, Feb. 2012.
- Akram, M.U., et al. (2012) Automated detection of exu-dates in colored retinal images for diagnosis of diabetic retinopathy. Applied Optics, 51, 4858-4866.
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