Impact of Retinal Image Preprocessing on Diabetic Retinopathy Classification Using Prototypical Networks
DOI:
https://doi.org/10.37859/coscitech.v7i1.11126
Abstract
Diabetic retinopathy is a diabetes complication that can lead to progressive retinal damage and permanent blindness. Early detection through automated fundus image classification is essential but challenged by varying image quality, background noise, and color dominance that reduces lesion visibility. Prototypical networks have demonstrated good performance in few-shot learning settings, yet specialized preprocessing is rarely explored. This study proposes a prototypical network enhanced with modified circle crop to remove irrelevant regions and enhanced green channel to improve microvascular lesion contrast. Experiments were conducted on the APTOS 2019 dataset consisting of 3,662 images, split into 2,929 training and 733 testing samples, using a 5-way 5-shot configuration. The proposed preprocessing increases accuracy from 64.53 percent to 71.35 percent and improves quadratic weighted kappa from 0.5712 to 0.6990. These results indicate that preprocessing enhances feature representation and classification performance under limited data conditions.
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