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2025
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| Online Access: | https://doi.org/10.5281/zenodo.15285258 |
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| author | Dr.R.V.V.S.V.Prasad, Kondaveti Raja, Sunkara Dhana Rajeswari, Pichika Harika, Kurella Manikanta |
| author_facet | Dr.R.V.V.S.V.Prasad, Kondaveti Raja, Sunkara Dhana Rajeswari, Pichika Harika, Kurella Manikanta |
| contents | <p>Diabetic Retinopathy (DR) is a severe ocular complication resulting from diabetes, characterized by damage<br>to the retinal blood vessels. This condition can occur in individuals with either type 1 or type 2 diabetes and<br>is exacerbated by prolonged hyperglycemia. As the retinal vessels deteriorate, they may become blocked or<br>leak, leading to compromised blood supply, loss of vision, and, in some cases, irreversible damage due to<br>the formation of scar tissue. The conventional approach to examining fundus images for DR diagnosis is<br>often cumbersome and time-consuming, requiring significant manual analysis to detect subtle differences in<br>retinal morphology.In this study, we propose a Customized Convolutional Neural Network (CCNN) as an<br>advanced deep learning technique for the automated detection of Diabetic Retinopathy. Our methodology<br>follows a structured workflow encompassing essential phases such as input data retrieval, data<br>preprocessing, segmentation, feature extraction, model creation, training, testing, and interpretation of<br>results. By employing this systematic approach, we aim to enhance the efficiency and accuracy of DR<br>detection, ultimately contributing to improved patient outcomes. The performance evaluation is conducted<br>using the MESSIDOR dataset, which includes 560 images for training and 163 images for testing. Our<br>proposed model achieved a notable test accuracy of 97.24%, indicating a significant improvement over<br>existing algorithms in terms of detection accuracy. The experimental results underline the potential of deep<br>learning models in revolutionizing the traditional diagnostic process, allowing for faster and more reliable<br>assessments of Diabetic Retinopathy. Through this research, we not only highlight the importance of<br>leveraging advanced machine learning techniques in medical diagnostics but also provide insights into the<br>potential future applications of such technologies in broader healthcare settings. By reducing the reliance on<br>manual examination methods, our CCNN approach presents a viable solution to the pressing challenges<br>posed by Diabetic Retinopathy diagnosis and management. </p> |
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| id | zenodo_https___doi_org_10_5281_zenodo_15285258 |
| institution | Zenodo |
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| publishDate | 2025 |
| publisher | Zenodo |
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| spellingShingle | Retinal Image Analysis for Diabetic Retinopathy Detection Using Convolutional Neural Network Dr.R.V.V.S.V.Prasad, Kondaveti Raja, Sunkara Dhana Rajeswari, Pichika Harika, Kurella Manikanta <p>Diabetic Retinopathy (DR) is a severe ocular complication resulting from diabetes, characterized by damage<br>to the retinal blood vessels. This condition can occur in individuals with either type 1 or type 2 diabetes and<br>is exacerbated by prolonged hyperglycemia. As the retinal vessels deteriorate, they may become blocked or<br>leak, leading to compromised blood supply, loss of vision, and, in some cases, irreversible damage due to<br>the formation of scar tissue. The conventional approach to examining fundus images for DR diagnosis is<br>often cumbersome and time-consuming, requiring significant manual analysis to detect subtle differences in<br>retinal morphology.In this study, we propose a Customized Convolutional Neural Network (CCNN) as an<br>advanced deep learning technique for the automated detection of Diabetic Retinopathy. Our methodology<br>follows a structured workflow encompassing essential phases such as input data retrieval, data<br>preprocessing, segmentation, feature extraction, model creation, training, testing, and interpretation of<br>results. By employing this systematic approach, we aim to enhance the efficiency and accuracy of DR<br>detection, ultimately contributing to improved patient outcomes. The performance evaluation is conducted<br>using the MESSIDOR dataset, which includes 560 images for training and 163 images for testing. Our<br>proposed model achieved a notable test accuracy of 97.24%, indicating a significant improvement over<br>existing algorithms in terms of detection accuracy. The experimental results underline the potential of deep<br>learning models in revolutionizing the traditional diagnostic process, allowing for faster and more reliable<br>assessments of Diabetic Retinopathy. Through this research, we not only highlight the importance of<br>leveraging advanced machine learning techniques in medical diagnostics but also provide insights into the<br>potential future applications of such technologies in broader healthcare settings. By reducing the reliance on<br>manual examination methods, our CCNN approach presents a viable solution to the pressing challenges<br>posed by Diabetic Retinopathy diagnosis and management. </p> |
| title | Retinal Image Analysis for Diabetic Retinopathy Detection Using Convolutional Neural Network |
| url | https://doi.org/10.5281/zenodo.15285258 |