Detection of Diabetic Retinopathy and Maculopathy in Eye Fundus Images Using Deep Learning and Image Augmentation

Rahim, Sarni Suhaila; Palade, Vasile; Almakky, Ibrahim; Holzinger, Andreas

doi:10.1007/978-3-030-29726-8_8

Cited by 17 publications

(5 citation statements)

References 31 publications

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“…For the proposed multimodal self-supervised pre-training, we use 59 retinography-angiography pairs from the public Isfahan MISP database (Alipour, Rabbani, & Akhlaghi, 2012). In this dataset, half of the images correspond to patients diagnosed with diabetic retinopathy, an eye condition that arises as a complication of diabetes (Rahim, Palade, Almakky, & Holzinger, 2019;Rahim, Palade, Jayne, Holzinger, & Shuttleworth, 2015). The other half of the images correspond to healthy individuals.…”

Section: Multimodal Reconstruction Pre-trainingmentioning

confidence: 99%

Self-supervised multimodal reconstruction pre-training for retinal computer-aided diagnosis

Hervella

Rouco

Novo

2021

Expert Systems with Applications

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Section: Multimodal Reconstruction Pre-trainingmentioning

confidence: 99%

Self-supervised multimodal reconstruction pre-training for retinal computer-aided diagnosis

Hervella

Rouco

Novo

2021

Expert Systems with Applications

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“…Rahim et al [29] explores the ability to improve classification efficiency through image pre-processing methods. In the proposed model, retinal images of Kaggle diabetic retinopathy dataset is firstly processed with a series of image pre-processing methods for promoting model classification while retaining as many of the original image features as possible.…”

Section: Image Pre-processingmentioning

confidence: 99%

A New Early Stage Diabetic Retinopathy Diagnosis Model Using Deep Convolutional Neural Networks and Principal Component Analysis

Mohammedhasan¹,

Uğuz²

2020

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Diabetic retinopathy (DR) is a disease of the retina, which leads over time to vision problems such retinal detachment, vitreous hemorrhage, glaucoma, and in worse cases leads to blindness, which can initially be controlled by periodic DR-screening. Early diagnosis will lead to greater control of the disease, whereas performing retinal examinations on all diabetic patients is an unattainable need, as diabetes is a chronic disease and its global prevalence has been steadily increasing over the past few decades. According to recent World Health Organization statistics, about 422 million people worldwide have diabetes, the majority living in low-and middle-income countries. This paper proposes a new strategy that brings the strength of convolutional neural networks (CNNs) to the diagnosis of DR. Coupled with using principal component analysis (PCA) that performs dimension reduction to improve the diagnostic accuracy, the proposed model exploiting edge-preserving guided image filtering (E-GIF) that performs as a contrast enhancement mechanism, and in addition to smoothing low gradient areas, it also accentuates strong edges. Diabetic retinopathy causes progressive damage to the blood vessels in the retina to the extent that it leaves traces and lesions in the tissues of the retina. These lesions appear in the form of edges and when processing retinal images, we seek to accentuate these edges to enable better diagnosis of diabetic retinopathy symptoms. A new CNN architecture with residual connections is used, which performs very well in diagnosing DR. The proposed model is named with RUnet-PCA: Residual U-net Deep CNN with Principal Component Analysis. The well-known AlexNet, VggNet-s, VggNet-16, VggNet-19, GoogleNet, and ResNet models were adopted for comparison with the proposed model. Publicly available Kaggle dataset was employed for training exploring the DR diagnosis accuracy. Experimental results show that the proposed RUnet-PCA model achieved a diagnosis accuracy of 98.44% and it was extremely robust and promising in comparison to other diagnosis methods.

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“…DR is involved in 2.6% of blindness worldwide [7]. Patients who have been suffering from uncontrolled diabetes for a long time are more likely to have the DR. Diabetics should have regular retina screenings to identify and treat DR early enough to avert blindness [8]. The presence of various lesions on a retina picture, for instance, Micro aneurysms (MA), internal bleeding, and hard and soft exudates (EX), is used to detect the DR [9,10].…”

Section: Introductionmentioning

confidence: 99%

A Faster RCNN-Based Diabetic Retinopathy Detection Method Using Fused Features From Retina Images

Nur-A-Alam,

Nasir,

Ahsan

et al. 2023

IEEE Access

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Early identification of diabetic retinopathy (DR) is critical as it shows few symptoms at the primary stages due to the nature of its gradual and slow growth. DR must be detected at the early stage to receive appropriate treatment, which can prevent the condition from escalating to severe vision loss problems. The current study proposes an automatic and intelligent system to classify DR or normal condition from retina fundus images (FI). Firstly, the relevant FIs were pre-processed, followed by extracting discriminating features using histograms of oriented gradient (HOG), Shearlet transform, and Region-Based Convolutional Neural Network (RCNN) from FIs and merging them as one fused feature vector. By using the fused features, a machine learning (ML) based faster RCNN classifier was employed to identify the DR condition and DR lesions. An extended experiment was carried out by employing binary classification (normal and DR) from three publicly available datasets. With a testing accuracy of 98.58%, specificity of 97.12%, and sensitivity of 95.72%, this proposed faster RCNN deep learning technique with feature fusion ensured a satisfactory performance in identifying the DR compared to the relevant state-ofthe-art works. By using a generalization validation strategy, this fusion-based method achieved a competitive performance with a detection accuracy of 95.75%.

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Detection of Diabetic Retinopathy and Maculopathy in Eye Fundus Images Using Deep Learning and Image Augmentation

Cited by 17 publications

References 31 publications

Self-supervised multimodal reconstruction pre-training for retinal computer-aided diagnosis

Self-supervised multimodal reconstruction pre-training for retinal computer-aided diagnosis

A New Early Stage Diabetic Retinopathy Diagnosis Model Using Deep Convolutional Neural Networks and Principal Component Analysis

A Faster RCNN-Based Diabetic Retinopathy Detection Method Using Fused Features From Retina Images

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