Tunable Wavelet Unit based Convolutional Neural Network in Optical Coherence Tomography Analysis Enhancement for Classifying Type of Epiretinal Membrane Surgery
Journal:
arXiv
Published Date:
Jul 1, 2025
Abstract
In this study, we developed deep learning-based method to classify the type
of surgery performed for epiretinal membrane (ERM) removal, either internal
limiting membrane (ILM) removal or ERM-alone removal. Our model, based on the
ResNet18 convolutional neural network (CNN) architecture, utilizes
postoperative optical coherence tomography (OCT) center scans as inputs. We
evaluated the model using both original scans and scans preprocessed with
energy crop and wavelet denoising, achieving 72% accuracy on preprocessed
inputs, outperforming the 66% accuracy achieved on original scans. To further
improve accuracy, we integrated tunable wavelet units with two key adaptations:
Orthogonal Lattice-based Wavelet Units (OrthLatt-UwU) and Perfect
Reconstruction Relaxation-based Wavelet Units (PR-Relax-UwU). These units
allowed the model to automatically adjust filter coefficients during training
and were incorporated into downsampling, stride-two convolution, and pooling
layers, enhancing its ability to distinguish between ERM-ILM removal and
ERM-alone removal, with OrthLattUwU boosting accuracy to 76% and PR-Relax-UwU
increasing performance to 78%. Performance comparisons showed that our AI model
outperformed a trained human grader, who achieved only 50% accuracy in
classifying the removal surgery types from postoperative OCT scans. These
findings highlight the potential of CNN based models to improve clinical
decision-making by providing more accurate and reliable classifications. To the
best of our knowledge, this is the first work to employ tunable wavelets for
classifying different types of ERM removal surgery.
