Deep Neural Network for Diabetic Retinopathy Detection

Diabetic retinopathy damages the retina of the patient. It is most frequent in the patients who have had diabetes for longer than 10 years. This problem is occurring in millions of people worldwide but medical practitioners and the tools required for detection of diabetic retinopathy is scare for serving the mass population. The work already was done to serve this problem using the application of machine learning but the efficiency of machine learning algorithm depends on the quality of feature extraction which requires domain knowledge. The work presented in this paper overcome the problem by using deep learning algorithm which automatically identifies the pattern and classifies the retina images into one of the five class based.

[1]  Kehua Guo,et al.  MADP: An Open and Scalable Medical Auxiliary Diagnosis Platform , 2019 .

[2]  Sven Loncaric,et al.  Detection of exudates in fundus photographs using convolutional neural networks , 2015, 2015 9th International Symposium on Image and Signal Processing and Analysis (ISPA).

[3]  D. Jeyakumari,et al.  Multiclass SVM-based automated diagnosis of diabetic retinopathy , 2013, 2013 International Conference on Communication and Signal Processing.

[4]  A. Motala,et al.  Global estimates of undiagnosed diabetes in adults. , 2014, Diabetes research and clinical practice.

[5]  M. Ulbig,et al.  Diabetic retinopathy - ocular complications of diabetes mellitus. , 2015, World journal of diabetes.

[6]  U. Acharya,et al.  Automatic identification of diabetic maculopathy stages using fundus images , 2009, Journal of medical engineering & technology.

[7]  Chua Kuang Chua,et al.  AUTOMATED CHARACTERIZATION AND DETECTION OF DIABETIC RETINOPATHY USING TEXTURE MEASURES , 2015 .

[8]  Jennifer K. Sun,et al.  Diabetic retinopathy: current understanding, mechanisms, and treatment strategies. , 2017, JCI insight.

[9]  Hulya Yalcin,et al.  Plant classification using convolutional neural networks , 2016, 2016 Fifth International Conference on Agro-Geoinformatics (Agro-Geoinformatics).

[10]  U. Rajendra Acharya,et al.  Automated Identification of Diabetic Retinopathy Stages Using Digital Fundus Images , 2008, Journal of Medical Systems.

[11]  Y. Nishida,et al.  Optical coherence tomography angiography in eyes with good visual acuity recovery after treatment for optic neuritis , 2017, PloS one.

[12]  Philip J. Morrow,et al.  Algorithms for digital image processing in diabetic retinopathy , 2009, Comput. Medical Imaging Graph..

[13]  Prachi Gharpure,et al.  Diabetic retinopathy detection using deep convolutional neural networks , 2016, 2016 International Conference on Computing, Analytics and Security Trends (CAST).

[14]  S. Vander Hoorn,et al.  Relationship between retinal nerve fiber layer and visual field sensitivity as measured by optical coherence tomography in chiasmal compression. , 2006, Investigative ophthalmology & visual science.

[15]  Matthew D. Davis,et al.  Proposed international clinical diabetic retinopathy and diabetic macular edema disease severity scales. , 2003, Ophthalmology.

[16]  U. Rajendra Acharya,et al.  Application of Higher Order Spectra for the Identification of Diabetes Retinopathy Stages , 2008, Journal of Medical Systems.

[17]  A. Grzybowski,et al.  Diabetic Retinopathy Screening Methods and Programmes Adopted in Different Parts of the World – Further Insights , 2015 .

[18]  T. Williamson,et al.  Automatic detection of diabetic retinopathy using an artificial neural network: a screening tool. , 1996, The British journal of ophthalmology.