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image of CNN-Based Integrated Framework for Enhanced Diabetic Retinopathy Detection

Abstract

Diabetic retinopathy (DR) is an ocular condition that adversely affects the retinal region of the eye. Without early detection, this disease can progress to irreversible blindness, particularly in individuals with diabetes. The manifestation of DR correlates with the stage of diabetes, categorized into five distinct stages: 0, 1, 2, 3, and 4. Noteworthy, symptoms characterize each stage during DR analysis. Machine Learning (ML) serves as a crucial tool for identifying intricate patterns within dataset inputs. Given its complexity and time-intensive nature, ML approaches have become integral in existing processes. In this study, diverse filtering techniques are applied to facilitate image filtration. Leveraging a Convolutional Neural Network (CNN), the detection of DR is executed based on its stages, with a specific focus on highlighting the regions affected by infection. The experimentation involves DR fundus color images, constituting a dataset of 1000 color fundus images. To enhance the proposed approach's performance, a Gabor filtering technique is incorporated, resulting in notably superior outcomes in terms of performance metrics.

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/content/journals/rascs/10.2174/0126662558293742240925044450
2024-10-16
2025-01-13

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/content/journals/rascs/10.2174/0126662558293742240925044450
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CNN-Based Integrated Framework for Enhanced Diabetic Retinopathy Detection
Bentham Science Publishers ; https://doi.org/10.2174/0126662558293742240925044450
/content/journals/rascs/10.2174/0126662558293742240925044450
/content/journals/rascs/10.2174/0126662558293742240925044450
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  • Article Type:     Research Article
Keywords: gabor filtering ; machine learning ; fundus color images ; convolutional neural network ; image filtration ; Diabetic retinopathy

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