A diagnosis model for detection and classification of diabetic retinopathy using deep learning

Syed, Saba Raoof; M A, Saleem Durai

doi:10.1007/s13721-023-00432-3

Cited by 5 publications

(1 citation statement)

References 39 publications

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“…especially complex neural networks, are often considered "black boxes". Saba Raoof [5] Mainly proposed the classification based on Data Preprocessing: Clean and preprocess the retinal images including resizing, Normalization. A diagnosis model for detection and classification of diabetic retinopathy using deep learning.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2024

IJFMR

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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.

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Section: Introductionmentioning

confidence: 99%

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

et al. 2024

IJFMR

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RLeU-Net: Segmentation of blood vessels in retinal fundus images for Diabetic Retinopathy Screening

Addanki,

Sumathi

2024

Multimed Tools Appl

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Land use/land cover (LULC) classification using deep-LSTM for hyperspectral images

Tejasree,

Agilandeeswari

2024

The Egyptian Journal of Remote Sensing and Space Sciences

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A diagnosis model for detection and classification of diabetic retinopathy using deep learning

Cited by 5 publications

References 39 publications

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

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

RLeU-Net: Segmentation of blood vessels in retinal fundus images for Diabetic Retinopathy Screening

Land use/land cover (LULC) classification using deep-LSTM for hyperspectral images

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