Retinal Fundus Image Analysis for Accurate Detection of Diabetic Retinopathy
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Diabetic Retinopathy (DR) is one of the most common causes of preventable blindness among diabetic patients worldwide. Early detection and timely treatment are essential to prevent severe vision impairment. However, manual screening of retinal fundus images is a time-consuming process that requires expert ophthalmologists and may lead to diagnostic inconsistencies. Recent advancements in artificial intelligence and medical image analysis have enabled the development of automated diagnostic systems capable of assisting clinicians in detecting retinal abnormalities. This research proposes an en- hanced fundus image analysis framework for accurate detection of diabetic retinopathy using advanced image preprocessing and deep learning techniques. The proposed system incorporates image enhancement methods including noise removal, contrast limited adaptive histogram equalization, and image normalization to improve the visibility of retinal lesions such as microa- neurysms, hemorrhages, and exudates. A convolutional neural network (CNN) architecture is employed to automatically extract discriminative features and classify retinal images into different stages of diabetic retinopathy. The model is trained and evaluated using publicly available retinal image datasets. Experimental results demonstrate that the proposed approach achieves high classification accuracy and improved sensitivity compared to traditional machine learning approaches. The system provides a reliable computer-aided diagnostic tool for large-scale screening programs and can significantly assist ophthalmologists in early detection of diabetic retinopathy. Future research will focus on integrating explainable artificial intelligence techniques to improve interpretability and clinical acceptance of automated diagnostic systems.
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