An improved method using supervised learning technique for diabetic retinopathy detection
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