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The aim of this research paper is to propose a system for the automated detection of Diabetic Retinopathy (DR) and Diabetic Macular Edema (DME) using fundus images. The proposed system utilizes image processing techniques to extract features such as blood vessels, exudates, microaneurysms, hemorrhages, fractal dimension, entropy, and homogeneity. These features are then fed to several classifiers and analyzed via machine learning techniques. The system can accurately detect the presence of DR and DME, which are associated with abnormalities such as microaneurysms, exudates, hemorrhages, and blood vessels in the fundus images. Currently, detecting these diseases is a time-consuming and manual process that requires a trained clinician. However, with the proposed system, we can automate the detection process, which can significantly improve the detection rate and reduce the time and cost required for diagnosis. The proposed system can be particularly useful in areas where the rate of diabetes in local populations is high, but the expertise and equipment required for manual detection are often lacking.
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Cite Article:
"Quantification of Retinal Tissue Damage", International Journal of Science & Engineering Development Research (www.ijrti.org), ISSN:2455-2631, Vol.8, Issue 6, page no.202 - 212, June-2023, Available :http://www.ijrti.org/papers/IJRTI2306036.pdf
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2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator