Facilitating deep learning through preprocessing of optical coherence tomography images.

ABSTRACT:

Background

While deep learning has delivered promising results in the field of ophthalmology, the hurdle to complete a deep learning study is high. In this study, we aim to facilitate small scale model trainings by exploring the role of preprocessing to reduce computational burden and accelerate learning.

Methods

A small subset of a previously published dataset containing optical coherence tomography images of choroidal neovascularization, drusen, diabetic macula edema, and normal macula was modified using Fourier transformation and bandpass filter, producing high frequency images, original images, and low frequency images. Each set of images was trained with the same model, and their performances were compared.

Results

Compared to that with the original image dataset

SUBMITTER: Li A 

PROVIDER: S-EPMC10108538 | biostudies-literature | 2023 Apr

REPOSITORIES: biostudies-literature