Year: 2024
Type: article
Abstract: Deep learning solutions can be used to classify pathological changes of the human retina visualized in OCT images. Available datasets that can be used to train neural network models include OCT images (B-scans) of classes with selected pathological changes and images of the healthy retina. These images often require correction due to improper acquisition or intensity variations related to the type of OCT device. This article provides a detailed assessment of the impact of preprocessing on classification efficiency. The histograms of OCT images were examined and, depending on the histogram distribution, incorrect image fragments were removed. At the same time, the impact of histogram equalization using the standard method and the Contrast-Limited Adaptive Histogram Equalization (CLAHE) method was analyzed. The most extensive dataset of Labeled Optical Coherence Tomography (LOCT) images was used for the experimental studies. The impact of changes was assessed for different neural network architectures and various learning parameters, assuming classes of equal size. Comprehensive studies have shown that removing unnecessary white parts from the input image combined with CLAHE improves classification accuracy up to as much as 4.75% depending on the used network architecture and optimizer type.
Source: Electronics
Authors: Tomasz Marciniak, Agnieszka Stankiewicz
Institution: Poznań University of Technology
Language: English
Topic: Retinal Imaging and Analysis
Subfield: Radiology, Nuclear Medicine and Imaging
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $2,728
Funder: Politechnika Poznańska
Award: G5251320294
OpenAlex ID: W4405572425