Deep learning architecture “LightOCT” for diagnostic decision support using optical coherence tomography images of biological samples
Optical coherence tomography (OCT) is being increasingly adopted as a label-free and non-invasive technique for biomedical applications such as cancer and ocular disease diagnosis. Diagnostic information for these tissues is manifest in textural and geometric features of the OCT images which are used by human expertise to interpret and triage. However, it suffers delays due to long process of conventional diagnostic procedure and shortage of human expertise. Here, a custom deep learning architecture, LightOCT, is proposed for classification of OCT images into diagnostically relevant classes. LightOCT is a convolutional neural network with only 2 convolutional layers and a fully connected ...
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