Fundus Image Classification: A Wavelet Feature Descriptor Approach
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Date
2023
Authors
Journal Title
Journal ISSN
Volume Title
Publisher
IEEE Xplore
Abstract
Lately, many diabetic patients are experiencing
diabetic retinopathy resulting in a loss of their sight. Even
though the urgency and threat posed by this condition, there is
insufficient data source to engage appropriate computational
intelligence tools. The few that exist happen to be imbalanced.
Leveraging on this imbalanced dataset, several activities have
been carried out to propose improved detection and
classification descriptors. Although some works have been done
in this domain, the issue of accuracy still persists in the
administration of an effective diagnosis. This paper harnessed
the benefits of Gabor filters and the multi-resolution property
of Discrete Wavelet Transforms (DWTs) to construct
appropriate fundus feature descriptors. These discriminant
features are fed into some selected but predominant classical
machine learning classifiers. Numerical evaluation of the study
gave a perfect (100%) average score for the fundus image
classification using Gradient Boosting and Logistic Regression
classifiers over Accuracy, F1-score, Precision and Recall
evaluation metric. The tie in performance is further broken
using their computation time, suggesting that Logistic
Regression is more appropriate with 9min 32sec over Gradient
Boosting or 1hr 10min 32sec.
Description
Research Article
Keywords
Retinopathy, Discrete Wavelet Transform, Gabor feature extraction, Gradient Boosting, Logistic Regression