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Wavelet scattering transform application in classification of retinal abnormalities using OCT images.

Zahra Baharlouei¹, Hossein Rabbani², Gerlind Plonka³

¹Medical Image and Signal Processing Research Center, School of Advanced Technologies in Medicine, Isfahan University of Medical Sciences, Isfahan, Iran.

Scientific Reports

|November 4, 2023

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This study introduces a Convolutional Neural Network using Wavelet Scattering Transform (WST) for diagnosing retinal abnormalities in Optical Coherence Tomography (OCT) images. The WST-based model achieves high accuracy with reduced computational complexity compared to deep learning methods.

Area of Science:

Ophthalmology
Medical Imaging
Computer Vision

Background:

Computer-Aided Diagnosis (CAD) aids ophthalmologists in detecting retinal abnormalities.
Optical Coherence Tomography (OCT) is a key imaging modality for retinal analysis.
Deep learning methods often require significant computational resources.

Purpose of the Study:

To develop an efficient Convolutional Neural Network (CNN) model for detecting retinal abnormalities using OCT images.
To leverage Wavelet Scattering Transform (WST) for improved feature extraction and reduced computational complexity.
To evaluate the model's performance on multiple public datasets and compare it with existing state-of-the-art methods.

Main Methods:

A two-layer Convolutional Neural Network incorporating Wavelet Scattering Transform (WST) was designed.

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WST was employed to generate sparse, translation-invariant, and deformation-stable image representations.

Principal Component Analysis (PCA) was utilized for feature classification.

The model was validated on four publicly available OCT datasets (OCTID, TOPCON, Heidelberg, Duke).

Main Results:

The WST-based CNN achieved high classification accuracies on the OCTID dataset (e.g., [Formula: see text] for 5 classes).
Accurate detection of Diabetic Macular Edema (DME) was demonstrated on the TOPCON dataset ([Formula: see text]).
Strong performance was observed on the Heidelberg ([Formula: see text]) and Duke ([Formula: see text]) datasets, classifying DME, Age-related Macular Degeneration, and Normal cases.
The model demonstrated competitive or superior performance compared to state-of-the-art methods with significantly lower computational complexity.

Conclusions:

The proposed WST-based CNN offers an efficient and effective approach for diagnosing retinal abnormalities from OCT images.
This method provides a valuable tool for ophthalmologists, potentially improving diagnostic speed and accuracy.
The model's reduced computational demands make it a practical solution for clinical applications.