  In the application of deep learning on optical coherence tomography (OCT)
data, it is common to train classification networks using 2D images originating
from volumetric data. Given the micrometer resolution of OCT systems,
consecutive images are often very similar in both visible structures and noise.
Thus, an inappropriate data split can result in overlap between the training
and testing sets, with a large portion of the literature overlooking this
aspect. In this study, the effect of improper dataset splitting on model
evaluation is demonstrated for three classification tasks using three OCT
open-access datasets extensively used, Kermany's and Srinivasan's ophthalmology
datasets, and AIIMS breast tissue dataset. Results show that the classification
performance is inflated by 0.07 up to 0.43 in terms of Matthews Correlation
Coefficient (accuracy: 5% to 30%) for models tested on datasets with improper
splitting, highlighting the considerable effect of dataset handling on model
evaluation. This study intends to raise awareness on the importance of dataset
splitting given the increased research interest in implementing deep learning
on OCT data.
