An improved method using supervised learning technique for diabetic retinopathy detection

Sabyasachi Chakraborty1, Gopal Chandra Jana1, Divya Kumari1, Aleena Swetapadma1
1School of Computer Engineering, KIIT University, Bhubaneswar, Odisha, 751024, India

Tóm tắt

Từ khóa


Tài liệu tham khảo

Kocur I, Resnikoff S (2002) Visual impairment and blindness in Europe and their prevention. Br J Ophthalmol 86:716–722

Evans J, Rooney C, Ashwood F, Dattani N, Wormald R (1996) Blindness and partial sight in England and Wales: April 1990–March 1991. Health Trends 28:5–12

Kanth S, Jaiswal A, Kakkar M (2013) Identification of different stages of diabetic retinopathy using artificial neural network. In: Sixth international conference on contemporary computing

Sil Kar S, Maity S (2018) Automatic detection of retinal lesions for screening of diabetic retinopathy. IEEE Trans Biomed Eng 65:608–618

Agarwal S, Acharjya K, Sharma S, Pandita S (2016) Automatic computer aided diagnosis for early diabetic retinopathy detection and monitoring: a comprehensive review. In: Online international conference on green engineering and technologies, pp 1–7

Roychowdhury S, Koozekanani D, Parhi K (2014) DREAM: diabetic retinopathy analysis using machine learning. IEEE J Biomed Health Inf 18:1717–1728

Adarsh P, Jeyakumari D (2013) Multiclass SVM-based automated diagnosis of diabetic retinopathy. In: International conference on communications and signal processing, pp 206–210

Acharya U, Lim C, Ng E, Chee C, Tamura T (2009) Computer-based detection of diabetes retinopathy stages using digital fundus images. P I Mech Eng H 223:545–553

Acharya R, Chua C, Ng E, Yu W, Chee C (2008) Application of higher order spectra for the identification of diabetes retinopathy stages. J Med Syst 32:481–488

Gardner G, Keating D, Williamson T, Elliott A (1996) Automatic detection of diabetic retinopathy using an artificial neural network: a screening tool. Brit J Ophthalmol 80:940–944

Xu J, Zhang X, Chen H, Li J, Zhang J, Shao L, Wang G (2018) Automatic analysis of microaneurysms turnover to diagnose the progression of diabetic retinopathy. IEEE Access 6:9632–9642

Habib M, Welikala R, Hoppe A, Owen C, Rudnicka A, Barman S (2017) Detection of microaneurysms in retinal images using an ensemble classifier. Inf Med Unlocked 9:44–57

Costa P, Galdran A, Smailagic A, Campilho A (2018) A weakly-supervised framework for interpretable diabetic retinopathy detection on retinal images. IEEE Access 6:18747–18758

Zhou L, Zhao Y, Yang J, Yu Q, Xu X (2018) Deep multiple instance learning for automatic detection of diabetic retinopathy in retinal images. IET Image Proc 12:563–571

Yadav A, Swetapadma A (2015) A single ended directional fault section identifier and fault locator for double circuit transmission lines using combined wavelet and ANN approach. Int J Electr Power Energy Syst 69:27–33

UCI machine learning repository. https://archive.ics.uci.edu/ml/datasets/Diabetic+Retinopathy+Debrecen+Data+Set

Balint A, Andras H (2014) An ensemble-based system for automatic screening of diabetic retinopathy. Knowl Based Syst 60:20–27