Dansingani, Kunal K and Vupparaboina, Kiran Kumar and Devarkonda, Surya Teja and Jana, Soumya and Chhablani, Jay and Freund, K Bailey
(2018)
Amplitude-scan classification using artificial neural networks.
Scientific Reports, 8 (1).
pp. 1-7.
ISSN 2045-2322
Abstract
Optical coherence tomography (OCT) images semi-transparent tissues noninvasively. Relying on
backscatter and interferometry to calculate spatial relationships, OCT shares similarities with other
pulse-echo modalities. There is considerable interest in using machine learning techniques for
automated image classifcation, particularly among ophthalmologists who rely heavily on diagnostic
OCT.Artifcial neural networks (ANN) consist of interconnected nodes and can be employed as
classifers after training on large datasets. Conventionally, OCT scans are rendered as 2D or 3D humanreadable
images of which the smallest depth-resolved unit is the amplitude-scan refectivity-function
profle which is difcult for humans to interpret. We set out to determine whether amplitude-scan
refectivity-function profles representing disease signatures could be distinguished and classifed by a
feed-forward ANN. Our classifer achieved high accuracies after training on only 24 eyes, with evidence
of good generalization on unseen data. The repertoire of our classifer can now be expanded to include
rare and unseen diseases and can be extended to other disciplines and industries.
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