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Segmentation of hard exudates in fundus images to detect diabetic retinopathy using modified U-NET
1 Department of CSE, Osmania University, Hyderabad, India.
2 Department of CSE, Maturi Venkata Subba Rao (MVSR) Engineering, College, Hyderabad, India.
Research Article
International Journal of Science and Research Archive, 2023, 10(01), 1069–1075.
Article DOI: 10.30574/ijsra.2023.10.1.0731
Publication history:
Received on 06 August 2023; revised on 14 October 2023; accepted on 29 October 2023
Abstract:
Diabetic retinopathy (DR) is a severe complication of diabetes, leading to potential vision loss due to damage to the retinal blood vessels. Hard exudates, which are visible lesions in fundus images, serve as critical indicators for diagnosing and monitoring DR. This study introduces a Modified U-Net architecture designed to improve the segmentation of hard exudates, thereby enhancing the detection and management of diabetic retinopathy.The U-Net model, renowned for its effectiveness in biomedical image segmentation, is adapted with several enhancements to better address the complexities of fundus images. These modifications include advanced feature extraction techniques, integration of attention mechanisms to focus on significant areas, and refined post-processing methods. These improvements aim to increase the accuracy and reliability of hard exudate segmentation.The Modified U-Net is evaluated on a dataset of fundus images with annotated hard exudates, using performance metrics such as accuracy, precision, recall, and the Dice coefficient. The results reveal that the Modified U-Net significantly outperforms traditional U-Net models and other contemporary segmentation methods. This enhanced model not only achieves higher accuracy in detecting and segmenting hard exudates but also improves the overall sensitivity and specificity.
Keywords:
Funds Images; Diabetic Retinopathy; Hard Exudates Segmentation; Feature Extraction; Attention Mechanisms; Modified U-Net
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