Cataract classification based on traditional data augmentation methods: Review study
DOI:
https://doi.org/10.29304/jqcsm.2026.18.22773Keywords:
, Deep learning, Cataract classification, Fundus Images, Class imbalance, Data augmentationAbstract
Cataract is a major cause of visual impairment and blindness worldwide. This has led to the design of automated systems for early detection through retinal fundus imaging. Recently, deep learning techniques with a focus on CNNs have shown good results for cataract classification. However, the results obtained from these models are often limited due to the inherent class imbalance problem in most medical image classification problems. To overcome the class imbalance problem, several researchers have adopted traditional data augmentation techniques to increase the number of the minority class. This review aims to highlight the recent studies that have adopted traditional data augmentation techniques for the classification of cataracts. It presents an overview of the recent research trends for the classification of cataracts using traditional data augmentation techniques.Downloads
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