  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 the long process of the conventional diagnostic procedure and shortage of
human expertise. Here, a custom deep learning architecture, LightOCT, is
proposed for the classification of OCT images into diagnostically relevant
classes. LightOCT is a convolutional neural network with only two convolutional
layers and a fully connected layer, but it is shown to provide excellent
training and test results for diverse OCT image datasets. We show that LightOCT
provides 98.9% accuracy in classifying 44 normal and 44 malignant (invasive
ductal carcinoma) breast tissue volumetric OCT images. Also, >96% accuracy in
classifying public datasets of ocular OCT images as normal, age-related macular
degeneration and diabetic macular edema. Additionally, we show ~96% test
accuracy for classifying retinal images as belonging to choroidal
neovascularization, diabetic macular edema, drusen, and normal samples on a
large public dataset of more than 100,000 images. The performance of the
architecture is compared with transfer learning based deep neural networks.
Through this, we show that LightOCT can provide significant diagnostic support
for a variety of OCT images with sufficient training and minimal
hyper-parameter tuning. The trained LightOCT networks for the
three-classification problem will be released online to support transfer
learning on other datasets.
