Detection And Classification Of Diabetic Retinopathy Using Adaptive Boosting And Artificial Neural Network
Volume 3 - Issue 8, August 2019 Edition
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Ashwin Dhakal; Laxmi Prasad Bastola; Subarna Shakya
AdaBoost, Artificial Neural Network, Diabetic Retinopathy, Histogram equalization, Median Filtering
The disorders identified with retina of the eye such as: Diabetic Retinopathy (DR), age-related Macular Degeneration (AMD), Glaucoma, etc. can cause visual impairments. The retinal fundus pictures of the patients can be acquired with a computerized fundus camera. It can then be used as machine learning application against the manual technique for the detection and prevention of DR. In this research, the retinal fundus image obtained from Himalaya Eye Hospital Pokhara, MESSIDOR database and EyePACS are used for the detection of DR along with its severity label. In the preprocessing phase, the images are first converted to grayscale images. Histogram equalization technique is performed to adjust image intensity in order to enhance contrast. Median filtering, a non-linear digital filtering technique, is implemented to remove noise present in the image. It too helps to improve the result for further processing. Blood vessels are detected and optic discs are identified and removed. Exudates are segmented for further processing. Finally, selected features are extracted for classification. The classification by Adaptive Boosting (AdaBoost) classifier using selected features achieved precision: 0.642, sensitivity: 0.870, accuracy: 0.620 and F1-score: 0.739. While the Artificial Neural Network achieved an accuracy of 98.43% for the detection of Diabetic retinopathy and 84.21% for the severity classification as stage 0: No-DR, 1: Mild-DR, 2: Moderate-DR, 3: Severe-DR and 4: Proferative-DR.
 Ding J, Wong TY. Current epidemiology of diabetic retinopathy and diabetic macular edema. Curr Diab Rep. 2012;12:346-54.
 Zheng Y, He M, Congdon N. The worldwide epidemic of diabetic retinopathy. Indian J Ophthalmol. 2012;60:428-31.
 Bhattarai MD. Epidemic of Non Communicable Diseases and its control. Kathmandu Univ Med J. 2012;10:1-3.
 Paudyal G, Shrestha MK, Meyer JJ, Thapa R, Gurung R, Ruit S. Prevalence of diabetic retinopathy following a community screening for diabetes. Nepal Med Col J 2008;10:160-3.
 Shrestha MK, Paudyal G, Wagle RR, Gurung R, Ruit S, Onta SR. Prevalence of and actors associated with diabetic retinopathy among diabetics in Nepal: a hospital based study: Nepal Med Col J. 2007;9:225-9.
 Yau JWY, Rogers SL, Kawasaki R, et al.; Meta-Analysis for Eye Disease (META-EYE) Study Group. Global prevalence and major risk factors of diabetic retinopathy. Diabetes Care 2012;35:556–564 pmid:22301125.
 Aiello LM. Perspectives on diabetic retinopathy. Am J Ophthalmol 2003; 136:122. PubMed PMID: 12834680.
 Jones CD, Greenwood RH, Misra A, Bachmann MO. Incidence and progression of diabetic retinopathy during 17 years of a population-based screening program in England. Diabetes Care 2012;35:592-6.
 Dodson PM (ed.) Diabetic Retinopathy: Screening to Treatment. Oxford: Oxford University Press, 2008.
 K. Verma, P. Deep and A. Ramakrishnan, "Detection and Classification of Diabetic Retinopathy using Retinal Images", IEEE, 2014.
 Labhade, L. Chouthmo and S. Deshmukh, "Diabetic Retinopathy Detection Using Soft Computing Techniques", International Conference on Automatic Control and Dynamic Optimization Techniques, pp. 175-178, 2016.
 S.Giraddi, J Pujari, S.Seeri, “Identifying Abnormalities in the Retinal Images using SVM Classifiers”, International Journal of Computer Applications(0975-8887), Volume 111 –No.6,(2015).
 R.Priya, P.Aruna, “SVM and Neural Network based Diagnosis of Diabetic Retinpathy”, International Journal of computer Applications(00975-8887), volume 41 -No.1,(March 2012).
 M.Melinscak.P.Prentasic, S.Loncaric, “Retinal Vessel Segmentation using Deep Neural Networks”, VISAPP(1), (2015):577-582.
 Mrinal Haloi, “Improved Microaneurysm detection using Deep Neural Networks”, Cornel University Library(2015), arXiv:1505.04424.
 E M Shahin, T E Taha, W Al-Nuaimy, S.El Raaie, O F Zahran, F E Abd El-Samie, “Automated Detection of Diabetic Retinopathy in Blurred Digital Fundus Images”, IEEE International Computer Engineering Conference , pages-20-25,(2012).
 Xiang chen et al, “A novel method for automatic hard exudates detection in color retinal images”, Proceedings of the 2012 International Conference on Machine Learning and Cybernetics, Xian (2012).
 Vesna Zeljkovi et al, “Classification Algorithm of Retina Images of Diabetic patients Based on Exudates Detection”, 978-1 -4673-2362-8/12, IEEE(2012).
 Ashwin Dhakal, Subarna Shakya "Image-Based Plant Disease Detection with Deep Learning" International Journal of Engineering Trends and Technology 61.1 (2018): 26-29. doi: 10.14445/22312803/IJCTT-V61P105.