Automatic Feature Learning to Grade Nuclear Cataracts Based on Deep Learning

  • Xinting GaoEmail author
  • Stephen Lin
  • Tien Yin Wong
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9004)


Cataracts are a clouding of the lens and the leading cause of blindness worldwide. Assessing the presence and severity of cataracts is essential for diagnosis and progression monitoring, as well as to facilitate clinical research and management of the disease. Existing automatic methods for cataract grading utilize a predefined set of image features that may provide an incomplete, redundant, or even noisy representation. In this work, we propose a system to automatically learn features for grading the severity of nuclear cataracts from slit-lamp images. Local filters learned from image patches are fed into a convolutional neural network, followed by a set of recursive neural networks to further extract higher-order features. With these features, support vector regression is applied to determine the cataract grade. The proposed system is validated on a large population-based dataset of \(5378\) images, where it outperforms the state-of-the-art by yielding with respect to clinical grading a mean absolute error (\(\varepsilon \)) of \(0.322\), a \(68.6\,\%\) exact integral agreement ratio (\(R_0\)), a \(86.5\,\%\) decimal grading error \(\le \)0.5 (\(R_{e0.5}\)), and a \(99.1\,\%\) decimal grading error \(\le \)1.0 (\(R_{e1.0}\)).


Support Vector Regression Convolutional Neural Network Anterior Cortex Nuclear Cataract Convolutional Layer 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Agency for Science, Technology and ResearchInstitute for Infocomm ResearchSingaporeSingapore
  2. 2.Microsoft ResearchBeijingPeople’s Republic of China
  3. 3.Singapore Eye Research InstituteSingaporeSingapore

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