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. 2024 May 24;14(11):1093.
doi: 10.3390/diagnostics14111093.

Classification of Diabetic Retinopathy Disease Levels by Extracting Spectral Features Using Wavelet CNN

Sumod Sundar  1 , Sumathy Subramanian  2 , Mufti Mahmud  3   4   5
Affiliations

Classification of Diabetic Retinopathy Disease Levels by Extracting Spectral Features Using Wavelet CNN

Sumod Sundar et al. Diagnostics (Basel). .

Abstract

Diabetic retinopathy (DR) arises from blood vessel damage and is a leading cause of blindness on a global scale. Clinical professionals rely on examining fundus images to diagnose the disease, but this process is frequently prone to errors and is tedious. The usage of computer-assisted techniques offers assistance to clinicians in detecting the severity levels of the disease. Experiments involving automated diagnosis employing convolutional neural networks (CNNs) have produced impressive outcomes in medical imaging. At the same time, retinal image grading for detecting DR severity levels has predominantly focused on spatial features. More spectral features must be explored for a more efficient performance of this task. Analysing spectral features plays a vital role in various tasks, including identifying specific objects or materials, anomaly detection, and differentiation between different classes or categories within an image. In this context, a model incorporating Wavelet CNN and Support Vector Machine has been introduced and assessed to classify clinically significant grades of DR from retinal fundus images. The experiments were conducted on the EyePACS dataset and the performance of the proposed model was evaluated on the following metrics: precision, recall, F1-score, accuracy, and AUC score. The results obtained demonstrate better performance compared to other state-of-the-art techniques.

Keywords: Wavelet CNN; classification; computer-aided diagnosis; convolutional neural network; diabetic retinopathy; spectral features.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Proposed model for the classification of DR from fundus image.
Figure 2
Figure 2
Representative images from the dataset—healthy eye (left) and eye with DR (right).
Figure 3
Figure 3
Latent vector space representation using t-SNE.
Figure 4
Figure 4
Training accuracy of Wavelet CNN.
Figure 5
Figure 5
Training–validation accuracy of (a) XGBoost and (b) random forest.
Figure 6
Figure 6
Confusion matrix: (a) Wavelet CNN, (b) XGBoost, (c) random forest, and (d) SVM.
Figure 7
Figure 7
ROC curves: (a) Wavelet CNN, (b) XGBoost, (c) random forest, and (d) SVM.
Figure 8
Figure 8
Accuracy comparison of the proposed model with other models using the EyePACS dataset [44,45,46].
See this image and copyright information in PMC

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