Automated Staging of Diabetic Retinopathy Using a 2D Convolutional Neural Network

Shaban, Mohamed; Ogur, Zeliha; Shalaby, Ahmed; Mahmoud, Ali; Ghazal, Mohammed; Sandhu, Harpal S.; Kaplan, Henry J.; El-Baz, Ayman

doi:10.1109/isspit.2018.8642686

Cited by 11 publications

(7 citation statements)

References 12 publications

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“…Shaban et al classified 4 different DR stages with the CNN model they designed. The study achieved an accuracy of 80.2% and a sensitivity of 78.7% [7].…”

Section: Related Workmentioning

confidence: 78%

Diabetic Retinopathy Detection with Deep Transfer Learning Methods

Çınarer

Kiliç

2021

Intelligent and Fuzzy Techniques for Emerging Conditions and Digital Transformation

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Diabetic retinopathy is an eye disease that occurs with damage to the retina and has many different complications, ranging from permanent blindness. The aim of this study is to develop a (convolutional neural network) CNN model that determines with high accuracy whether fundus images are diabetic retinopathy. The performance of the model has been verified in Kaggle APTOS 2019 dataset with AlexNET and VggNET-16 deep transfer learning algorithms. Various image processing techniques have been used as well as deep learning methods to further improve the classification performance. Images in the data set were rescaled to 224 × 224 × 3 and converted to Grayscale color space. Besides Gauss filter applied to eliminate the noise in the images. The area under the curve (AUC), precision, recall, and accuracy metrics of the deep transfer learning models used in this study were compared. The AlexNet model achieved a 98.6% AUC score, 95.2% accuracy, and the VggNET-16 model achieved a 99.6% AUC score and 98.1% accuracy. VggNET-16 was found to have higher confidence. Our results show that with the correct optimization of the CNN model applied in diabetic retinopathy classification, deep transfer learning models can achieve high performance and can be used in the detection of diabetic retinopathy patients.

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“…Shaban et al classified 4 different DR stages with the CNN model they designed. The study achieved an accuracy of 80.2% and a sensitivity of 78.7% [7].…”

Section: Related Workmentioning

confidence: 78%

Diabetic Retinopathy Detection with Deep Transfer Learning Methods

Çınarer

Kiliç

2021

Intelligent and Fuzzy Techniques for Emerging Conditions and Digital Transformation

View full text Add to dashboard Cite

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“…The modeling has been implemented over the Kaggle dataset, which has large fundus images where the outcome shows better accuracy. Adoption of CNN was also reported in Shaban et al [35], where multiclass classification of diabetic retinopathy is carried out for four different stages.…”

Section: Related Workmentioning

confidence: 89%

“…However, the classification process can be further improved if more appropriate features are extracted in due processing and analysis steps. The proposed scheme has adopted the Kaggle eye dataset [35], which consists of a higher number of fundus retinal images characterized by higher resolution. The presented method of classification makes use of CNN to perform the determination of variable states of diabetic retinopathy.…”

Section: Methodsmentioning

confidence: 99%

Novel Framework for Enhanced Learning-based Classification of Lesion in Diabetic Retinopathy

Prakruthi¹,

Komarasamy²

2022

IJACSA

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Diabetic retinopathy is an adverse medical condition resulting from a high level of blood sugar potentially affecting the retina and leading to permanent vision loss in its advanced stage of progression. A literature review is conducted to assess the effectiveness of existing approaches to find that Convolution Neural Network (CNN) has been frequently adopted for analyzing the fundus retinal image for detection and classification. However, existing scientific methods are mainly inclined towards achieving accuracy in their learning techniques without much deeper investigation of possibilities to improve the methodology of type using CNN. Therefore, the proposed scheme introduces a computational framework where a simplified feature enhancement operation is carried out, resulting in artifact-free images with better features. The enhanced image is then subjected to CNN to perform multiclass categorization of potential stages of diabetic retinopathy to see if it outperforms existing schemes.

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“…Machine learning techniques have been used in DR detection and classification [4][5][6][7][8][9][10][11][12][13][14]. Acharya et al introduced an automated diagnosis method using SVM classifier to identify normal, mild DR, moderate DR, severe DR, and prolific DR [4].…”

Section: Introductionmentioning

confidence: 99%

“…Furthermore, Shaban et al introduced a CNN trained on 101 fundus images that can accurately identify the four stages of the disease (i.e. non-DR, NPDR, severe NPDR and PDR) [8]. A leave-one-out approach was used for testing.…”

Section: Introductionmentioning

confidence: 99%

A convolutional neural network for the screening and staging of diabetic retinopathy

et al. 2020

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Diabetic retinopathy (DR) is a serious retinal disease and is considered as a leading cause of blindness in the world. Ophthalmologists use optical coherence tomography (OCT) and fundus photography for the purpose of assessing the retinal thickness, and structure, in addition to detecting edema, hemorrhage, and scars. Deep learning models are mainly used to analyze OCT or fundus images, extract unique features for each stage of DR and therefore classify images and stage the disease. Throughout this paper, a deep Convolutional Neural Network (CNN) with 18 convolutional layers and 3 fully connected layers is proposed to analyze fundus images and automatically distinguish between controls (i.e. no DR), moderate DR (i.e. a combination of mild and moderate Non Proliferative DR (NPDR)) and severe DR (i.e. a group of severe NPDR, and Proliferative DR (PDR)) with a validation accuracy of 88%-89%, a sensitivity of 87%-89%, a specificity of 94%-95%, and a Quadratic Weighted Kappa Score of 0.91-0.92 when both 5-fold, and 10-fold cross validation methods were used respectively. A prior pre-processing stage was deployed where image resizing and a class-specific data augmentation were used. The proposed approach is considerably accurate in objectively diagnosing and grading diabetic retinopathy, which obviates the need for a retina specialist and expands access to retinal care. This technology enables both early diagnosis and objective tracking of disease progression which may help optimize medical therapy to minimize vision loss.

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Automated Staging of Diabetic Retinopathy Using a 2D Convolutional Neural Network

Cited by 11 publications

References 12 publications

Diabetic Retinopathy Detection with Deep Transfer Learning Methods

Diabetic Retinopathy Detection with Deep Transfer Learning Methods

Novel Framework for Enhanced Learning-based Classification of Lesion in Diabetic Retinopathy

A convolutional neural network for the screening and staging of diabetic retinopathy

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