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3 articles for “eye fundus”
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Image Preprocessing and Analysis on Eye Fundus Images Segmentation by Using Density Clustering Methods
Abstract: In order to do an automated evaluation of various retinal illnesses such as Diabetic retinopathy, Glaucoma, and Macular Edema, fundus images must be pre-processed first. For many reasons, it's difficult to accurately detect the optic disc. Many blood vessels cross the optic disc, making it difficult to discern the disc's boundaries in fundus images. Lesion regions in diabetic retinopathy look very much like an optic disc's colour and texture, so …
Published in Recent Trends in Sensor Research & Technology Read article
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Enhancing Glaucoma Diagnosis with Deep Learning: A Study Using ResNet-50 and DenseNet-121
Abstract: Glaucoma is a leading cause of irreversible blindness worldwide, mainly resulting from progressive optic nerve damage, often related to elevated intraocular pressure. Early detection is essential to prevent vision loss, but traditional diagnostic methods rely on specialized equipment and trained professionals, making large-scale screening difficult. This study uses a publicly available fundus imaging dataset to explore the effectiveness of deep learning models for glaucoma detection. These datasets provide medical images, …
Published in International Journal of Brain Sciences · Vol. 2, Issue 2, 2025 · pp. 9–18 Read article
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The Analysis of Deep Learning-Based Methods for Identifying Diabetic Retinopathy
Abstract: Diabetic retinopathy (DR) is a degenerative eye condition resulting from diabetes mellitus, where high blood glucose levels lead to lesions on the retina. This condition is considered the leading cause of blindness among working-age diabetic patients, particularly in developing countries. As the disease is irreversible, the treatment aims to preserve the patient’s current vision. Early detection is crucial for effective management of DR to maintain vision. One of the main …
Published in Research and Reviews : A Journal of Medical Science and Technology · Vol. 13, Issue 3, 2024 · pp. 15–31 Read article