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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