International Journal For Multidisciplinary Research

E-ISSN: 2582-2160     Impact Factor: 9.24

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

Call for Paper Volume 6 Issue 5 September-October 2024 Submit your research before last 3 days of October to publish your research paper in the issue of September-October.

Classification of Diabetic Retinopathy Using Deep Convolutional Neural Networks (DCNN)

Author(s) Mahalakshmi Bollimuntha, Hyndavi p, Deepthi k, Srilatha P
Country India
Abstract Diabetic Retinopathy (DR) is a leading cause of vision impairment and blindness among individuals with diabetes. Early detection and accurate classification of DR stages are crucial for timely intervention and effective management. In recent years, Deep learning (DL) methods have emerged as powerful tools for image analysis, demonstrating remarkable success in various medical imaging applications. Large dataset, processing difficulty, complex training and computation time are the major drawbacks of existing work by using support vector machine (SVM) method. The objective of this proposed system gives proper results by using Deep Convolutional neural networks (DCNNs) techniques for the classification of Diabetic Retinopathy with high accuracy by using the feature analysis of blood vessels.
Keywords Diabetic Retinopathy, Deep learning, Support Vector Machine, Deep Convolutional Neural Network .
Field Engineering
Published In Volume 6, Issue 2, March-April 2024
Published On 2024-04-05
Cite This Classification of Diabetic Retinopathy Using Deep Convolutional Neural Networks (DCNN) - Mahalakshmi Bollimuntha, Hyndavi p, Deepthi k, Srilatha P - IJFMR Volume 6, Issue 2, March-April 2024. DOI 10.36948/ijfmr.2024.v06i02.16351
DOI https://doi.org/10.36948/ijfmr.2024.v06i02.16351
Short DOI https://doi.org/gtp8kp

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