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Cross-domain diabetic retinopathy detection model using deep learning
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Paper Abstract

Globally Diabetic retinopathy (DR) is one of the leading causes of blindness. But due to low patient to doctor ratio performing clinical retinal screening processes for all such patients is not always possible. In this paper a deep learning based automated diabetic retinopathy detection method is presented . Though different frameworks exist for classifying different retinal diseases with both shallow machine learning algorithms and deep learning algorithms, there is very little literature on the problem of variation of sources between training and test data. Kaggle EYEPACS data was used in this study for training the dataset and the Messidor dataset was used for testing the efficiency of the model. With proper data sampling, augmentation and pre-processing techniques it was possible to achieve state-of-the-art accuracy of classification using Messidor dataset (which had a different camera settings and resolutions of images). The model achieved significant performance with a sensitivity of almost 90% and specificity of 91. 94% with an average accuracy of 90. 4

Paper Details

Date Published: 6 September 2019
PDF: 7 pages
Proc. SPIE 11139, Applications of Machine Learning, 111390V (6 September 2019); doi: 10.1117/12.2529450
Show Author Affiliations
Sourya Sengupta, Univ. of Waterloo (Canada)
Amitojdeep Singh, Univ. of Waterloo (Canada)
John Zelek, Univ. of Waterloo (Canada)
Vasudevan Lakshminarayanan, Univ. of Waterloo (Canada)


Published in SPIE Proceedings Vol. 11139:
Applications of Machine Learning
Michael E. Zelinski; Tarek M. Taha; Jonathan Howe; Abdul A. S. Awwal; Khan M. Iftekharuddin, Editor(s)

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