Diabetic Retinopathy Early Detection Using Bag of Features Point Dependent Image Processing Methods

Indu Sharma

doi:10.55524/ijircst.2021.9.6.70

Abstract

Diabetic Retinopathy (DR) is a major problem that impairs anthropological eyesight. It is usually formed at the rear side of the eye retina, when light-sensitive tissues' blood vessels are damaged. Based on symptoms or diseases in the eye, such as mild visual difficulties, vision loss, blindness, damage to blood vessels in the retina caused by high blood sugar levels, etc., attention is required. Investigators have confronted a crucial test for the early identification of eye illness using image recognition methods for many years. A method for automatically identifying diabetic retinopathy based on topographies such as point-based from retinal pictures utilizing SVM and basket of topographies techniques. There are four steps in this procedure: The input eye retina picture is pre-processed in the first step. We smear re-scaling geometric changes like revolution for picture enhancement in the second phase. In the third stage, we present an automated technique based on point-based features and a list of pictures for first diabetes or non-diabetic diagnosis utilizing SVM and a basket of topographies such as Haar modification image processing methods. Lastly, we liken the measurements like RMSE, PSNR, Elapsed Time and Accuracy utilizing this future method.

Keywords

Diabetic Retinopathy (DR), Elapsed Time (ET), Haar Transformation, Peak Signal Noise Ratio (PSNR), Root Mean Square Error (RMSE), Support Vector Machine (SVM).

References

Agrawal T, Agrawal AK, Singh SK. An efficient key-accumulation cryptosystem for cloud. Int J Eng Adv Technol. 2019;
Mian SM, Kumar R. Review on Intend Adaptive Algorithms for Time Critical Applications in Underwater Wireless Sensor Auditory and Multipath Network. In: 2019 International Conference on Automation, Computational and Technology Management, ICACTM 2019. 2019.
Gola KK, Dhingra M, Rathore R. Modified version of playfair technique to enhance the security of plaintext and key using rectangular and substitution matrix. Int J Eng Adv Technol. 2019;
Tyagi S, Dwivedi RK, Saxena AK. A high capacity PDF text steganography technique based on hashing using quadratic probing. Int J Intell Eng Syst. 2019;
Cao P, Ren F, Wan C, Yang J, Zaiane O. Efficient multi-kernel multi-instance learning using weakly supervised and imbalanced data for diabetic retinopathy diagnosis. Comput Med Imaging Graph. 2018;
Dhingra M, Khan G, Gola KK. An improved version of playfair technique. Int J Innov Technol Explor Eng. 2019;
Tyagi S, Dwivedi RK, Saxena AK. High capacity steganography protected using shamir’s threshold scheme and permutation framework. Int J Innov Technol Explor Eng. 2019;
Sharma A, Sharma MK, Dwivedi RK. Hybrid neuro-fuzzy classification algorithm for social network. Int J Eng Adv Technol. 2019;
Meenu, Andeep Kumar S, Panchal VK, Kumar R. Evolution of new integrated haze removal algorithm based on haze line. Int J Eng Adv Technol. 2019;
Rastogi A, Singh R, Sharma R, Kalony SD. The survey of digital image analysis with artificial intelligence- DCNN technique. In: Proceedings of the 2020 9th International Conference on System Modeling and Advancement in Research Trends, SMART 2020. 2020.
Anand V. Photovoltaic actuated induction motor for driving electric vehicle. Int J Eng Adv Technol. 2019;8(6 Special Issue 3):1612–4.
Singh D. Robust controlling of thermal mixing procedure by means of sliding type controlling. Int J Eng Adv Technol. 2019;
Wu H, Zhang X, Geng X, Dong J, Zhou G. Computer aided quantification for retinal lesions in patients with moderate and severe non-proliferative diabetic retinopathy: A retrospective cohort study. BMC Ophthalmol. 2014;
Khatri M, Kumar A. Stability Inspection of Isolated Hydro Power Plant with Cuttlefish Algorithm. In: 2020 International Conference on Decision Aid Sciences and Application, DASA 2020. 2020.
Sharma K, Goswami L. RFID based Smart Railway Pantograph Control in a Different Phase of Power Line. In: Proceedings of the 2nd International Conference on Inventive Research in Computing Applications, ICIRCA 2020. 2020.
Goswami L, Kaushik MK, Sikka R, Anand V, Prasad Sharma K, Singh Solanki M. IOT Based Fault Detection of Underground Cables through Node MCU Module. In: 2020 International Conference on Computer Science, Engineering and Applications, ICCSEA 2020. 2020.
Usman Akram M, Khalid S, Tariq A, Khan SA, Azam F. Detection and classification of retinal lesions for grading of diabetic retinopathy. Comput Biol Med. 2014;
Saleh MD, Eswaran C. An automated decision-support system for non-proliferative diabetic retinopathy disease based on MAs and HAs detection. Comput Methods Programs Biomed. 2012;
Jha A, Kumar M. Two wheels differential type odometry for mobile robots. In: Proceedings of 3rd International Conference on Reliability, Infocom Technologies and Optimization. IEEE; 2014. p. 1–5.
Valverde C, Garcia M, Hornero R, Lopez-Galvez M. Automated detection of diabetic retinopathy in retinal images. Indian Journal of Ophthalmology. 2016.
Aldington SJ, Kohner EM, Meuer S, Klein R, SjØlie AK. Methodology for retinal photography and assessment of diabetic retinopathy: the EURODIAB IDDM Complications Study. Diabetologia. 1995;
Varsha Ramdas Pokharkar, Sangita Nikhumbh. Detection of MA and HA in Diabetic Retinopathy. Int J Eng Res. 2016;
Soto-Pedre E, Navea A, Millan S, Hernaez-Ortega MC, Morales J, Desco MC, et al. Evaluation of automated image analysis software for the detection of diabetic retinopathy to reduce the ophthalmologists’ workload. Acta Ophthalmol. 2015;
Tasgaonkar M, Khambete M. Using differential morphological profiles for microaneurysm detection in diabetic retinal fundus images. In: 2016 International Conference on Signal and Information Processing, IConSIP 2016. 2017.

Cites this article as

I. Sharma "Diabetic Retinopathy Early Detection Using Bag of Features Point Dependent Image Processing Methods", International Journal of Innovative Research in Engineering & Management (ijircst), Vol-9, Issue-6, Page No-320-325, 2021. Available from: https://doi.org/10.55524/ijircst.2021.9.6.70

Corresponding Author

Indu Sharma

RIMT University, Mandi Gobindgarh, Punjab, India (indusharma@rimt.ac.in)

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Diabetic Retinopathy Early Detection Using Bag of Features Point Dependent Image Processing Methods

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