Analysis of Detection of Diabetic Retinopathy using LPB and Deep Learning Techniques
|International Journal of Engineering Trends and Technology (IJETT)||
|© 2020 by IJETT Journal|
|Year of Publication : 2020|
|Authors : Manojkumar S B, H S Sheshadri
|DOI : 10.14445/22315381/IJETT-V68I12P221|
MLA Style: Manojkumar S B, H S Sheshadri. Analysis of Detection of Diabetic Retinopathy using LPB and Deep Learning Techniques International Journal of Engineering Trends and Technology 68.12(2020):123-131.
APA Style:Manojkumar S B, H S Sheshadri. Analysis of Detection of Diabetic Retinopathy using LPB and Deep Learning Techniques. International Journal of Engineering Trends and Technology, 68(12), 123-131.
Diabetes mellitus (DM) arises when not the pancreas secretes enough insulin or the body has been unable to absorb it adequately. This contributes to an abnormal rise in the blood level of glucose. Many human body parts would be affected. One of those is also among those eyes. This high glucose level causes damage to blood vessels over time. It is estimated that 95i percent of individuals who are determined to have diabetes have category 2. Around eighty percent of falls into category 2 cases are overweight. Diabetic Retinopathy (DR) disease leads to visual loss. Regular screening and treatment are needed. Many approaches have applied for the detection and identification of the DR from the past decade using some mathematical and image processing algorithms, features extraction techniques, and artificial neural network classification. Few are failed during the pre-processing stage, feature extraction stage, vessel extraction, and the classification stage. In this paper, it is identified all stages of diabetic retinopathy during the early stage by developing the regions of a retina image to show the specific region of interest in terms of its severity level by collecting the large data from Kaggle, DIARETDB0, and DIARETDB1 dataset and then pre-trained the models and applied to LPB and deep learning classifiers and able to obtain the results and analysis those hypothesis results and achieve 65% predicted correctly.
 M. Chetoui, M. A. Akhloufi, and M. Kardouchi, , Diabetic Retinopathy Detection Using Machine Learning and Texture Features, 2018 IEEE Canadian Conference on Electrical & Computer Engineering (CCECE), Quebec City, QC,4(2) (2018) 1-4.
 M. Tavakoli, R. Shahri, H. Pourreza, A. Mehdizadeh, T. Banaee and M. Bahreini Toosi, A complementary method for automated detection of microaneurysms in fluorescein angiography fundus images to assess diabetic retinopathy, Pattern Recognition, 46(10) (2013) 2740-2753.
 S. Qummar A Deep Learning Ensemble Approach for Diabetic Retinopathy Detection, in IEEE Access,7(3), (2019)150530-150539.
 S. Jan, I. Ahmad, S. Karim, Z. Hussain, M. Rehman, and M. A. Shah, Status of diabetic retinopathy and its presentation patterns in diabetics at ophthalmology clinics, J. Postgraduate Med. Inst. (Peshawar-Pakistan),32(1)( 2018)24-27.  J. Amin, M. Sharif, M. Yasmin, H. Ali and S. L. Fernandes, A method for the detection and classification of diabetic retinopathy using structural predictors of bright lesions, Journal of Computer Science., 19(4)(2017)153-164.
 R. A. Welikala, M. M. Fraz, J. Dehmeshki, A. Hoppe, V. Tah, S. Mann, Genetic algorithm-based feature selection combined with dual classification for the automated detection of proliferative diabetic retinopathy, Computerized Med. Image. Graph., 43(5)( 2015) 64-77.
 T. Ahonen, A. Hadid, and M. Pietikainen, Face description with local binary patterns: Application to face recognition, IEEE Trans. Pattern Anal. Mach. Intell.,28(12)(2006) 2037-2041.
 R. Girshick, J. Donahue, T. Darrell, and J. Malik, Rich feature hierarchies for accurate object detection and semantic segmentation, in Proceedings of the IEEE conference on computer vision and pattern recognition,5(4)(2014)580–587
 J. Lachure, A. Deorankar, S. Lachure, S. Gupta, and R. Jadhav, Diabetic Retinopathy using morphological operations and Machine learning, in 2015 IEEE International Advance Computing Conference (IACC),6(3)(2015)617–622.
 G. Gardner, D. Keating, T. H. Williamson, and A. T. Elliott, Automatic detection of diabetic retinopathy using an artificial neural network: a screening tool, British Journal of Ophthalmology,80(11)(1996)940–944.
 Shahin E.M, Taha T.E, Al-Nuaimy W, El Rabaie S, Automated detection of diabetic retinopathy in blurred digital fundus images, IEEE Transactions on Computer Engineering International Conference (ICENCO),10(3)(2012)20- 25.
 Sivakumar R, Ravindran G, Muthayya M, Lakshminarayanan S, "Diabetic retinopathy classification" IEEE Transactions on Convergent Technologies for the Asia-Pacific Region,1(4)(2003)205- 208.
 Lee, S.S. Rajeswari, M. Ramachandram, D. Shaharuddin, Screening of Diabetic Retinopathy - Automatic Segmentation of Optic Disc in Colour Fundus Images IEEE Transactions on Distributed Frameworks for Multimedia Applications,14(10)(2006) 1-7.
 R. Priya and P. Aruna, "Diagnosis of diabetic retinopathy using machine learning techniques," ICTACT Journal on soft computing,3(4)(2013)563–575.
 A. Krizhevsky, I. Sutskever, and G. E. Hinton, Imagenet classification with deep convolutional neural networks, in Advances in neural information processing systems,6(2)(2012)1097–1105.
 M. Penna, D.V. Gowda, J. J. Jijesh, and Shivashankar, Design and implementation of automatic medicine dispensing machine, in RTEICT 2017 - 2nd IEEE International Conference on Recent Trends in Electronics, Information and Communication Technology, Proceedings,3(3)(2018)1962–1966.
 V. Gowda, S. B. Sridhara, K. B. Naveen, M. Ramesha, and G. N. Pai, Internet of things: Internet revolution, impact, technology road map and features, Adv. Math. Sci. J., 9(7)(2020)4405–4414.
 P. Ramesh Naidu, N. Guruprasad, and V. D.Gowda, Design and implementation of crypto cloud system for securing files in the cloud, Adv. Math. Sci. J., 9(7)(2020)4485–4493.
 Li, Yung-Hui, Computer-Assisted Diagnosis For Diabetic Retinopathy Based On Fundus Images Using Deep Convolutional Neural Network. Mobile Information Systems,14(6)(2019)1-14.
 Mateen, Muhammad, Wen, Junhao, Hassan, Mehdi, Automatic Detection of Diabetic Retinopathy: A Review on Datasets, Methods and Evaluation Metrics, IEEE Access13 (4)(2020)1-8.
 Colomer, Adrián. Detection of Early Signs of Diabetic Retinopathy Based on Textural and Morphological Information in Fundus Images. Sensors (Basel, Switzerland)20(4)(2020)12-21.
 M. Ramesha, Dankan Gowda V, Sridhara S.B, Naveena Pai G, FPGA Implementation of Low Power High-Speed BTED Algorithm for 8 Bit Error Correction in Cryptography System, Int. J. Emerg. Trends Eng. Res.,8(7)(2020)3893–3897.
 M. Tavakoli, R. Shahri, H. Pourreza, A. Mehdizadeh, T. Banaee and M. Bahreini Toosi, A complementary method for automated detection of microaneurysms in fluorescein angiography fundus images to assess diabetic retinopathy, Pattern Recognition, 46(10)(2013)2740-2753.
 K. Noronha, U. R. Acharya, K. P. Nayak, S. Kamath, and S. V. Bhandary, Decision support system for diabetic retinopathy using discrete wavelet transform, Proc Inst Mech Eng H,227(3)(2013)251-261.
 V. Gulshan, L. Peng, M. Coram, M. Stumpe, D. Wu, A. Narayanaswamy, et al., Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs, JAMA,316(22)(2016)2402-2410.
 S. Regina Lourdhu Suganthi, U K Sneha, Shwetha S Diabetic Retinopathy Classification Using Machine Learning Techniques, International Journal of Engineering Trends and Technology 68(1) (2020) 51-56.
Diabetic Retinopathy, Hemorrhages, Exudates, Microaneurysms, NPDR.