Categorization of Diabetic Retinopathy using Deep Learning Techniques

Renukadevi, N. T.; Saraswathi, K.; Karunakaran, S.; Anguraj, B.

doi:10.1088/1757-899x/1055/1/012087

Cited by 4 publications

(3 citation statements)

References 8 publications

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“…The images were processed, and the images were artificially increased and modelled, and the model attained an accuracy of 90%. Gahyung et al [21] developed a fully automated classification for predicting diabetic retinopathy (DR) with the CNN model. Based on fluorescein angiography, salient facts of classification were verified.…”

Section: Related Workmentioning

confidence: 99%

Automatic Classification of Colour Fundus Images for Prediction Eye Disease Types Based on Hybrid Features

Shamsan,

Senan,

Shatnawi

2023

Diagnostics

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Early detection of eye diseases is the only solution to receive timely treatment and prevent blindness. Colour fundus photography (CFP) is an effective fundus examination technique. Because of the similarity in the symptoms of eye diseases in the early stages and the difficulty in distinguishing between the type of disease, there is a need for computer-assisted automated diagnostic techniques. This study focuses on classifying an eye disease dataset using hybrid techniques based on feature extraction with fusion methods. Three strategies were designed to classify CFP images for the diagnosis of eye disease. The first method is to classify an eye disease dataset using an Artificial Neural Network (ANN) with features from the MobileNet and DenseNet121 models separately after reducing the high dimensionality and repetitive features using Principal Component Analysis (PCA). The second method is to classify the eye disease dataset using an ANN on the basis of fused features from the MobileNet and DenseNet121 models before and after reducing features. The third method is to classify the eye disease dataset using ANN based on the fused features from the MobileNet and DenseNet121 models separately with handcrafted features. Based on the fused MobileNet and handcrafted features, the ANN attained an AUC of 99.23%, an accuracy of 98.5%, a precision of 98.45%, a specificity of 99.4%, and a sensitivity of 98.75%.

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Section: Related Workmentioning

confidence: 99%

Automatic Classification of Colour Fundus Images for Prediction Eye Disease Types Based on Hybrid Features

Shamsan,

Senan,

Shatnawi

2023

Diagnostics

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“…The framework achieves an accuracy of 98.0%, sensitivity of 98.7%, and specificity of 97.8% on the Kaggle dataset. Renukadevi et al [19] presented a density-related GoogLeNet model for detecting DR from the APTOS data set. The method was implemented in several stages: preprocessing, modelling, data augmentation, feature extraction, and classification through the last layer in a model.…”

Section: Plos Onementioning

confidence: 99%

“…The table shows the overall accuracy of all systems. It is noted that the best accuracy was achieved when feeding the hybrid features of the [13], Fouzia et al [14], Gadekallu et al [15], Ludwig et al [16], Ayushi et al [17], Renukadevi et al [19]: These studies focused on various deep learning and machine learning approaches, using different architectures and techniques for retinopathy diagnosis. Specific performance metrics vary across the studies, but accuracies range from 65.6% to 96.6%.…”

Section: Plos Onementioning

confidence: 99%

Predicting of diabetic retinopathy development stages of fundus images using deep learning based on combined features

Shamsan,

Senan,

Ahmad Shatnawi

2023

PLoS ONE

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The number of diabetic retinopathy (DR) patients is increasing every year, and this causes a public health problem. Therefore, regular diagnosis of diabetes patients is necessary to avoid the progression of DR stages to advanced stages that lead to blindness. Manual diagnosis requires effort and expertise and is prone to errors and differing expert diagnoses. Therefore, artificial intelligence techniques help doctors make a proper diagnosis and resolve different opinions. This study developed three approaches, each with two systems, for early diagnosis of DR disease progression. All colour fundus images have been subjected to image enhancement and increasing contrast ROI through filters. All features extracted by the DenseNet-121 and AlexNet (Dense-121 and Alex) were fed to the Principal Component Analysis (PCA) method to select important features and reduce their dimensions. The first approach is to DR image analysis for early prediction of DR disease progression by Artificial Neural Network (ANN) with selected, low-dimensional features of Dense-121 and Alex models. The second approach is to DR image analysis for early prediction of DR disease progression is by integrating important and low-dimensional features of Dense-121 and Alex models before and after PCA. The third approach is to DR image analysis for early prediction of DR disease progression by ANN with the radiomic features. The radiomic features are a combination of the features of the CNN models (Dense-121 and Alex) separately with the handcrafted features extracted by Discrete Wavelet Transform (DWT), Local Binary Pattern (LBP), Fuzzy colour histogram (FCH), and Gray Level Co-occurrence Matrix (GLCM) methods. With the radiomic features of the Alex model and the handcrafted features, ANN reached a sensitivity of 97.92%, an AUC of 99.56%, an accuracy of 99.1%, a specificity of 99.4% and a precision of 99.06%.

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Deep learning based computer-aided automatic prediction and grading system for diabetic retinopathy

Khanna

Singh

Thawkar

et al. 2023

Multimed Tools Appl

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Categorization of Diabetic Retinopathy using Deep Learning Techniques

Cited by 4 publications

References 8 publications

Automatic Classification of Colour Fundus Images for Prediction Eye Disease Types Based on Hybrid Features

Automatic Classification of Colour Fundus Images for Prediction Eye Disease Types Based on Hybrid Features

Predicting of diabetic retinopathy development stages of fundus images using deep learning based on combined features

Deep learning based computer-aided automatic prediction and grading system for diabetic retinopathy

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