Cross-domain diabetic retinopathy detection using deep learning

Sourya Sengupta; Amitojdeep Singh; John Zelek; Vasudevan Lakshminarayanan

doi:10.1117/12.2529450

6 September 2019 Cross-domain diabetic retinopathy detection using deep learning

Sourya Sengupta, Amitojdeep Singh, John Zelek, Vasudevan Lakshminarayanan

Proceedings Volume 11139, Applications of Machine Learning; 111390V (2019) https://doi.org/10.1117/12.2529450
Event: SPIE Optical Engineering + Applications, 2019, San Diego, California, United States

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

Citation Download Citation

Sourya Sengupta, Amitojdeep Singh, John Zelek, and Vasudevan Lakshminarayanan "Cross-domain diabetic retinopathy detection using deep learning", Proc. SPIE 11139, Applications of Machine Learning, 111390V (6 September 2019); https://doi.org/10.1117/12.2529450

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available